European Radiology

Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram

This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.

Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution

To determine the diagnostic performance of a deep learning (DL) model in evaluating myometrial invasion (MI) depth on T2-weighted imaging (T2WI)-based endometrial cancer (EC) MR imaging (ECM). We retrospectively enrolled 530 patients with pathologically proven EC at our institution between January 1, 2013, and December 31, 2017. All imaging data were reviewed on picture archiving and communication systems (PACS) server. Both sagittal and coronal T2WI-based MR images were used for lesion area determination. All MR images were divided into two groups: deep (more than 50%) and shallow (less than 50%) MI based on their pathological diagnosis. We trained a detection model based on YOLOv3 algorithm to locate the lesion area on ECM. Then, the detected regions were fed into a classification model based on DL network to identify MI depth automatically. In the testing dataset, the trained model detected lesion regions with an average precision rate of 77.14% and 86.67% in both sagittal and coronal images, respectively. The classification model yielded an accuracy of 84.78%, a sensitivity of 66.67%, a specificity of 87.50%, a positive predictive value of 44.44%, and a negative predictive value of 94.59% in determining deep MI. The radiologists and trained network model together yielded an accuracy of 86.2%, a sensitivity of 77.8%, a specificity of 87.5%, a positive predictive value of 48.3%, and a negative predictive value of 96.3%. In this study, the DL network model derived from MR imaging provided a competitive, time-efficient diagnostic performance in MI depth identification. • The models established with the deep learning method could help improve the diagnostic confidence and performance of MI identification based on endometrial cancer MR imaging. • The models enabled the classification of endometrial cancer MR images to the two categories with a sensitivity of 0.67, a specificity of 0.88, and an accuracy of 0.85. • Using the detected lesion region to evaluate myometrial invasion depth could remove redundant information in the image and provide more effective features.

Ovarian cancer staging and follow-up: updated guidelines from the European Society of Urogenital Radiology female pelvic imaging working group

Abstract Objective To provide up-to-date European Society of Urogenital Radiology (ESUR) guidelines for staging and follow-up of patients with ovarian cancer (OC). Methods Twenty-one experts, members of the female pelvis imaging ESUR subcommittee from 19 institutions, replied to 2 rounds of questionnaires regarding imaging techniques and structured reporting used for pre-treatment evaluation of OC patients. The results of the survey were presented to the other authors during the group’s annual meeting. The lexicon was aligned with the Society of American Radiology (SAR)-ESUR lexicon; a first draft was circulated, and then comments and suggestions from the other authors were incorporated. Results Evaluation of disease extent at diagnosis should be performed by chest, abdominal, and pelvic CT. The radiological report should map the disease with specific mention of sites that may preclude optimal cytoreductive surgery. For suspected recurrence, CT and [18F]FDG PET-CT are both valid options. MRI can be considered in experienced centres, as an alternative to CT, considering the high costs and the need for higher expertise in reporting. Conclusions CT is the imaging modality of choice for preoperative evaluation and follow-up in OC patients. A structured radiological report, including specific mention of sites that may preclude optimal debulking, is of value for patient management. Key Points Question Guidelines were last published for ovarian cancer (OC) imaging in 2010; here, guidance on imaging techniques and reporting, incorporating advances in the field, are provided. Findings Structured reports should map out sites of disease, highlighting sites that limit cytoreduction. For suspected recurrence, CT and 18FDG PET-CT are options, and MRI can be considered. Clinical relevance Imaging evaluation of OC patients at initial diagnosis (mainly based on CT), using a structured report that considers surgical needs is valuable in treatment selection and planning.

ESR Essentials: characterisation and staging of adnexal masses with MRI and CT—practice recommendations by ESUR

Abstract Ovarian masses encompass various conditions, from benign to highly malignant, and imaging plays a vital role in their diagnosis and management. Ultrasound, particularly transvaginal ultrasound, is the foremost diagnostic method for adnexal masses. Magnetic Resonance Imaging (MRI) is advised for more precise characterisation if ultrasound results are inconclusive. The ovarian-adnexal reporting and data system (O-RADS) MRI lexicon and scoring system provides a standardised method for describing, assessing, and categorising the risk of each ovarian mass. Determining a histological differential diagnosis of the mass may influence treatment decision-making and treatment planning. When ultrasound or MRI suggests the possibility of cancer, computed tomography (CT) is the preferred imaging technique for staging. It is essential to outline the extent of the malignancy, guide treatment decisions, and evaluate the feasibility of cytoreductive surgery. This article provides a comprehensive overview of the key imaging processes in evaluating and managing ovarian masses, from initial diagnosis to initial treatment. It also includes pertinent recommendations for properly performing and interpreting various imaging modalities. Key Points MRI is the modality of choice for indeterminate ovarian masses at ultrasound, and the O-RADS MRI lexicon and score enable unequivocal communication with clinicians. CT is the recommended modality for suspected ovarian masses to tailor treatment and surgery. Multidisciplinary meetings integrate information and help decide the most appropriate treatment for each patient.

Preoperative pelvic MRI and 2-[18F]FDG PET/CT for lymph node staging and prognostication in endometrial cancer—time to revisit current imaging guidelines?

Abstract Objective This study presents the diagnostic performance of four different preoperative imaging workups (IWs) for prediction of lymph node metastases (LNMs) in endometrial cancer (EC): pelvic MRI alone (IW1), MRI and [18F]FDG-PET/CT in all patients (IW2), MRI with selective [18F]FDG-PET/CT if high-risk preoperative histology (IW3), and MRI with selective [18F]FDG-PET/CT if MRI indicates FIGO stage ≥ 1B (IW4). Methods In 361 EC patients, preoperative staging parameters from both pelvic MRI and [18F]FDG-PET/CT were recorded. Area under receiver operating characteristic curves (ROC AUC) compared the diagnostic performance for the different imaging parameters and workups for predicting surgicopathological FIGO stage. Survival data were assessed using Kaplan-Meier estimator with log-rank test. Results MRI and [18F]FDG-PET/CT staging parameters yielded similar AUCs for predicting corresponding FIGO staging parameters in low-risk versus high-risk histology groups (p ≥ 0.16). The sensitivities, specificities, and AUCs for LNM prediction were as follows: IW1—33% [9/27], 95% [185/193], and 0.64; IW2—56% [15/27], 90% [174/193], and 0.73 (p = 0.04 vs. IW1); IW3—44% [12/27], 94% [181/193], and 0.69 (p = 0.13 vs. IW1); and IW4—52% [14/27], 91% [176/193], and 0.72 (p = 0.06 vs. IW1). IW3 and IW4 selected 34% [121/361] and 54% [194/361] to [18F]FDG-PET/CT, respectively. Employing IW4 identified three distinct patient risk groups that exhibited increasing FIGO stage (p < 0.001) and stepwise reductions in survival (p ≤ 0.002). Conclusion Selective [18F]FDG-PET/CT in patients with high-risk MRI findings yields better detection of LNM than MRI alone, and similar diagnostic performance to that of MRI and [18F]FDG-PET/CT in all. Key Points • Imaging by MRI and [18F]FDG PET/CT yields similar diagnostic performance in low- and high-risk histology groups for predicting central FIGO staging parameters. • Utilizing a stepwise imaging workup with MRI in all patients and [18F]FDG-PET/CT in selected patients based on MRI findings identifies preoperative risk groups exhibiting significantly different survival. • The proposed imaging workup selecting ~54% of the patients to [18F]FDG-PET/CT yield better detection of LNMs than MRI alone, and similar LNM detection to that of MRI and [18F]FDG-PET/CT in all.

Prospective validation of the role of PET/CT in detecting disease after neoadjuvant chemotherapy in advanced ovarian cancer

Abstract Objectives The study aimed to compare the diagnostic accuracies of 2-[18F]FDG PET/CT and contrast-enhanced CT (ceCT) after neoadjuvant chemotherapy (NACT) in advanced ovarian cancer (OC). Materials and methods This study consisted historical observational cohort and prospective validation cohort. Patients with newly diagnosed stage III–IV OC scheduled for NACT were recruited, with imaging performed after three to six cycles of NACT before interval debulking surgery. Nineteen regions in the abdominopelvic cavity were scored for the presence and absence of disease, referenced to the intra-operative findings or histological specimens. Diagnostic metrics were compared using McNemar’s test. Results In the historical cohort (23 patients, age 58 ± 13), 2-[18F]FDG PET had an overall accuracy (Acc) 82%, sensitivity (Sen) 38%, specificity (Spe) 97%, positive predictive value (PPV) 79% and negative predictive value (NPV) 82%; ceCT had an overall Acc 86%, Sen 64%, Spe 93%, PPV 75% and NPV 89%. In the prospective cohort (46 patients, age 59 ± 9), 2-[18F] FDG PET had an overall Acc 87%, Sen 48%, Spe 98%, PPV 84% and NPV 88%; ceCT had an overall Acc 89%, Sen 66%, Spe 95%, PPV 77% and NPV 91%. No significant difference was demonstrated between the two imaging modalities (p > 0.05). High false-negative rates were observed in the right subdiaphragmatic space, omentum, bowel mesentery and serosa. High omental metabolic uptake after NACT was associated with histological non-responders (p < 0.05). Conclusion 2-[18F]FDG PET/CT had no additional value over ceCT with comparable diagnostic accuracy in detecting disease after NACT in advanced OC. Clinical relevance statement 2-[18F]FDG PET/CT is not superior to contrast-enhanced CT in determining disease after neoadjuvant chemotherapy in advanced ovarian cancer; contrast-enhanced CT should be suffice for surgical planning before interval debulking surgery. Key Points • Additional value of 2-[18F]FDG PET/CT over contrast-enhanced CT is undefined in detecting disease after neoadjuvant chemotherapy. • 2-[18F]FDG PET/CT has comparable diagnostic accuracy compared to contrast-enhanced CT. • Contrast-enhanced CT will be suffice for surgical planning after neoadjuvant chemotherapy.

Node-RADS for preoperative locoregional nodal staging of endometrial cancer: reproducibility and accuracy assessment using CT and MRI

Abstract Objectives To assess the reproducibility and diagnostic accuracy of the Node Reporting and Data System 1.0 (Node-RADS) for detecting pelvic nodal metastases by endometrial cancer (EC) using CT and MRI, among readers with different levels of expertise. Materials and methods This IRB-approved, single-center retrospective study included 128 patients with EC who underwent preoperative MRI at our Institution (Jan 2020–Dec 2023). Six readers with different levels of expertise in pelvic MRI (2 dedicated pelvic radiologists, 2 residents in their fourth year of training, and 2 residents in their second year of training) independently evaluated preoperative CTs and MRIs and assigned Node-RADS scores. Inter-observer agreement and inter-method agreement were calculated. Node-RADS was compared with post-surgical pathology data. Results At surgery, pelvic nodal metastases were detected in 12.5% of the patients. Interobserver agreement in nodal status assessment using Node-RADS varied from κ = 0.783 to κ = 0.426 using MRI, and from κ = 0.936 to κ = 0.295 using CT, with worse results among less experienced readers. MRI and CT were concordant in the N definition in 94–98% of the cases. Using MRI, the most experienced readers showed 63% sensitivity and 100% specificity in the detection of nodal metastases, compared to 44% sensitivity and 96% specificity for poorly experienced readers. Using CT, the most experienced readers showed 50% sensitivity and 100% specificity; the less experienced readers showed 43% sensitivity and 94% specificity. Conclusions Node-RADS is a reproducible and accurate tool for locoregional nodal staging of EC, but only for readers with specific experience in pelvic imaging. MRI outperforms CT in nodal assessment. Key Points Question Preoperative assessment of nodal metastases by EC is difficult, but it may help in tailoring the best surgical approach for each patient . Findings Node-RADS is a reliable tool for assessing the presence of pelvic nodal metastases by EC, both on CT and MRI, among experienced readers . Clinical relevance The use of Node-RADS among experienced readers enables detection of nodal metastases with good sensitivity and excellent specificity; MRI should be preferred over CT due to its higher sensitivity . Graphical Abstract

Ovarian cancer reporting lexicon for computed tomography (CT) and magnetic resonance (MR) imaging developed by the SAR Uterine and Ovarian Cancer Disease-Focused Panel and the ESUR Female Pelvic Imaging Working Group

Imaging evaluation is an essential part of treatment planning for patients with ovarian cancer. Variation in the terminology used for describing ovarian cancer on computed tomography (CT) and magnetic resonance (MR) imaging can lead to ambiguity and inconsistency in clinical radiology reports. The aim of this collaborative project between Society of Abdominal Radiology (SAR) Uterine and Ovarian Cancer (UOC) Disease-focused Panel (DFP) and the European Society of Uroradiology (ESUR) Female Pelvic Imaging (FPI) Working Group was to develop an ovarian cancer reporting lexicon for CT and MR imaging. Twenty-one members of the SAR UOC DFP and ESUR FPI working group, one radiology clinical fellow, and two gynecologic oncology surgeons formed the Ovarian Cancer Reporting Lexicon Committee. Two attending radiologist members of the committee prepared a preliminary list of imaging terms that was sent as an online survey to 173 radiologists and gynecologic oncologic physicians, of whom 67 responded to the survey. The committee reviewed these responses to create a final consensus list of lexicon terms. An ovarian cancer reporting lexicon was created for CT and MR Imaging. This consensus-based lexicon has 6 major categories of terms: general, adnexal lesion-specific, peritoneal carcinomatosis-specific, lymph node-specific, metastatic disease -specific, and fluid-specific. This lexicon for CT and MR imaging evaluation of ovarian cancer patients has the capacity to improve the clarity and consistency of reporting disease sites seen on imaging. • This reporting lexicon for CT and MR imaging provides a list of consensus-based, standardized terms and definitions for reporting sites of ovarian cancer on imaging at initial diagnosis or follow-up. • Use of standardized terms and morphologic imaging descriptors can help improve interdisciplinary communication of disease extent and facilitate optimal patient management. • The radiologists should identify and communicate areas of disease, including difficult to resect or potentially unresectable disease that may limit the ability to achieve optimal resection.

Preoperative 18F-FDG PET/CT tumor markers outperform MRI-based markers for the prediction of lymph node metastases in primary endometrial cancer

Abstract Objectives To compare the diagnostic accuracy of preoperative 18F-FDG PET/CT and MRI tumor markers for prediction of lymph node metastases (LNM) and aggressive disease in endometrial cancer (EC). Methods Preoperative whole-body 18F-FDG PET/CT and pelvic MRI were performed in 215 consecutive patients with histologically confirmed EC. PET/CT-based tumor standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and PET-positive lymph nodes (LNs) (SUVmax > 2.5) were analyzed together with the MRI-based tumor volume (VMRI), mean apparent diffusion coefficient (ADCmean), and MRI-positive LN (maximum short-axis diameter ≥ 10 mm). Imaging parameters were explored in relation to surgicopathological stage and tumor grade. Receiver operating characteristic (ROC) curves were generated yielding optimal cutoff values for imaging parameters, and regression analyses were used to assess their diagnostic performance for prediction of LNM and progression-free survival. Results For prediction of LNM, MTV yielded the largest area under the ROC curve (AUC) (AUC = 0.80), whereas VMRI had lower AUC (AUC = 0.72) (p = 0.03). Furthermore, MTV > 27 ml yielded significantly higher specificity (74%, p < 0.001) and accuracy (75%, p < 0.001) and also higher odds ratio (12.2) for predicting LNM, compared with VMRI > 10 ml (58%, 62%, and 9.7, respectively). MTV > 27 ml also tended to yield higher sensitivity than PET-positive LN (81% vs 50%, p = 0.13). Both VMRI > 10 ml and MTV > 27 ml were significantly associated with reduced progression-free survival. Conclusions Tumor markers from 18F-FDG PET/CT outperform MRI markers for the prediction of LNM. MTV > 27 ml yields a high diagnostic performance for predicting aggressive disease and represents a promising supplement to conventional PET/CT reading in EC. Key Points • Metabolic tumor volume (MTV) outperforms other 18F-FDG PET/CT and MRI markers for preoperative prediction of lymph node metastases (LNM) in endometrial cancer patients. • Using cutoff values for tumor volume for prediction of LNM, MTV > 27 ml yielded higher specificity and accuracy than VMRI> 10 ml. • MTV represents a promising supplement to conventional PET/CT reading for predicting aggressive disease in EC.

Feasibility of T2WI-MRI-based radiomics nomogram for predicting normal-sized pelvic lymph node metastasis in cervical cancer patients

To investigate the feasibility of T Preoperative images of 219 normal-sized pathologically confirmed LNs from 132 cervical cancer patients admitted to our hospital between January 2013 and March 2020 were retrospectively reviewed. Regions of interests (ROIs) were separately delineated on whole LNs and tumors. The maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods were used for the construction of radiomics signature. Logistic regression modeling was employed to build models based on clinical features on LN T Both model 2 and model 3 showed higher diagnostic accuracy (training: model 2 0.75, model 3 0.78, model 1 0.72; validation: model 2 0.77, model 3 0.69, model 1 0.66) and AUC (training: model 2 0.77, model 3 0.82, model 1 0.74; validation: model 2 0.75, model 3 0.74, model 1 0.70) than clinical model 1. Diagnostic performance of model 3 was improved compared with model 2 in primary cohort, but reduced in validation cohort. However, the differences did not show obvious statistical difference (p = 0.05 and p = 0.15). T • The combination of LN radiomics signature with LN clinical morphological features on T

Ultrasound-guided high-intensity focused ultrasound for symptomatic uterine fibroids: clinical outcome of two European centers

Abstract Objectives The aim of this study is to assess the clinical outcome and mid-term efficacy of ultrasound-guided high-intensity focused ultrasound (USgHIFU) as a treatment for symptomatic uterine fibroids at two major European HIFU centers. Materials and methods This bi-center longitudinal clinical study involved the treatment of 100 patients with symptomatic uterine fibroids using USgHIFU (n = 59 in Germany, n = 41 in Bulgaria). Clinical outcomes were evaluated at 6 weeks, 6 months, and 1 year follow-up utilizing the uterine fibroid symptoms-quality of life questionnaire for fibroid-related symptoms and health-related quality of life as well as MRI imaging for determining the fibroid volume. Results The mean fibroid volume reduction rate was 33.2 ± 22.9%, 51.3 ± 24.2%, and 59.1 ± 28.0% at 6 weeks, 6 months, and 1 year, respectively (each p < 0.001). The mean symptom severity score decreased from 43.9 ± 18.8 at baseline to 35.4 ± 18.2 at 6 weeks, 31.1 ± 20.0 at 6 months, and 23.1 ± 14.0 at 1 year (each p < 0.001). The mean QOL score improved from 56.5 ± 23.4 at baseline to 65.4 ± 22.2 at 6 weeks, 72.5 ± 19.5 at 6 months, and 79.4 ± 15.3 at 1 year (each p < 0.001). No major complications were observed, though two patients experienced temporary sciatic nerve irritation following the procedure. Four patients had pregnancies and deliveries without any complications after USgHIFU therapy. Conclusion To our knowledge, this is the first longitudinal study conducted in two major European HIFU centers that reveals the clinical efficacy of USgHIFU ablation on symptomatic uterine fibroids. Our results confirm that USgHIFU is a non-invasive approach with a low risk of complications, offering an innovative treatment option for affected women. Key Points Question To evaluate mid-term clinical efficacy and safety of US-guided high-intensity focused ultrasound (HIFU) for treating symptomatic uterine fibroids and patient outcomes across two European centers. Findings US-guided HIFU treatment resulted in significant fibroid volume reduction (up to 59.1% after 1 year) improving symptoms and quality of life with no major complications. Clinical relevance This prospective longitudinal study provides preliminary data assessing mid-term efficacy and clinical outcomes of ultrasound-guided HIFU. It is shown to be a low-risk, non-invasive treatment option for symptomatic uterine fibroids that reduces fibroid size and improves patients’ quality of life.

Combination of HIFU with sulfur hexafluoride microbubbles in the treatment of solitary uterine fibroids: a systematic review and meta-analysis

To assess the efficacy and safety of sulfur hexafluoride microbubbles on ultrasound-guided high-intensity focused ultrasound (HIFU) ablation of uterine fibroids. Studies that compared HIFU-microbubble combination with HIFU-only in patients with uterine fibroids were searched from inception to April 2022. The standardized mean difference (SMD) or relative risk (RR) with 95% confidence interval (CI) for different outcome parameters was calculated. Seven studies were included, with a total of 901 patients (519 in the combination group and 382 in the HIFU-only group). The energy consumption for treating 1 cm Sulfur hexafluoride microbubbles can be safely used to enhance and accelerate the ablation effects of HIFU in the treatment of uterine fibroids. The combination of HIFU with sulfur hexafluoride microbubbles offers a promising non-invasive treatment option for patients with uterine fibroids. • Sulfur hexafluoride microbubbles combined with ultrasound-guided high-intensity focused ultrasound (USgHIFU) has potential advantages in the treatment of uterine fibroids. • Sulfur hexafluoride microbubbles not only enhance the effects of USgHIFU treatment for uterine fibroids but also shorten its duration. • Sulfur hexafluoride microbubbles do not increase the incidence of USgHIFU-related adverse events in the treatment of uterine fibroids.

No incision required for long-lasting symptom relief in a selection of women suffering from uterine fibroids

O-RADS MRI score: analysis of misclassified cases in a prospective multicentric European cohort

To retrospectively review the causes of categorization errors using O-RADS-MRI score and to determine the presumptive causes of these misclassifications. EURAD database was retrospectively queried to identify misclassified lesions. In this cohort, 1194 evaluable patients with 1502 pelvic masses (277 malignant / 1225 benign lesions) underwent standardized MRI to characterize adnexal masses with histology or 2 years' follow-up as a reference standard. An expert radiologist reviewed cases with two junior radiologists and lesions termed misclassified if malignant lesion was scored ≤ 3, a benign lesion was scored ≥ 4, the site of origin was incorrect, or a non-adnexal mass was incorrectly categorized as benign or malignant. There were 139 / 1502 (9.2%) misclassified masses in 116 women including 109 adnexal and 30 non-adnexal masses. False-negative cases corresponded to 16 borderline or invasive malignant adnexal masses rated score ≤ 3 (16 / 139, 11.5%). False-positive cases corresponded to 88 benign masses were rated score 4 (67 / 139, 48.2%) or 5 (18 / 139,12.9%) or considered suspicious non-adnexal lesions (3 / 139, 2.2%). Misclassifications were only due to origin error in 12 adnexal masses (8 benign, 4 malignant) (8.6%, 12 / 139) and 23 non-adnexal masses (18 benign, 5 malignant,16.5%, 23 / 139) perceived respectively as non-adnexal and adnexal masses. Interpretive error (n = 104), failure to recognize technical insufficient exams (n = 9), and perceptual errors (n = 4) were found. Most interpretive was due to misinterpretation of solid tissue or incorrect assignment of mass origin. Eighty-four out of 139 cases were correctly reclassified by the readers with strict adherence to the score rules. Most errors were due to misinterpretation of solid tissue or incorrect assignment of mass origin. • Prospective assignment of O-RADS-MRI score resulted in misclassification of 9.25% of sonographically indeterminate pelvic masses. • Most errors were interpretive (74.8%) due to misinterpretation of solid tissue as defined by the lexicon or incorrect assignment of mass origin. • Pelvic inflammatory disease is a common source of misclassification (8.9%) (12 / 139).

Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer

To develop a radiomics signature based on multisequence magnetic resonance imaging (MRI) to preoperatively predict peritoneal metastasis (PM) in ovarian cancer (OC). Eighty-nine patients with OC were divided into a training cohort including patients (n = 54) with a single lesion and a validation cohort including patients (n = 35) with bilateral lesions. Radiomics features were extracted from the T2-weighted images (T2WIs), fat-suppressed T2WIs, multi-b-value diffusion-weighted images (DWIs), and corresponding parametric maps. A radiomics signature and nomogram incorporating the radiomics signature and clinical predictors were developed and validated on the training and validation cohorts, respectively. The radiomics signature generated by 6 selected features showed a favorable discriminatory ability to predict PM in OC with an area under the curve (AUC) of 0.963 in the training cohort and an AUC of 0.928 in the validation cohort. The nomogram, comprising the radiomics signature, pelvic fluid, and CA-125 level, showed more favorable discrimination with an AUC of 0.969 in the training cohort and 0.944 in the validation cohort. Net reclassification index with values of 0.548 in the training cohort and 0.500 in the validation cohort. Radiomics signature based on multisequence MRI serves as an effective quantitative approach to predict PM in OC patients. A nomogram of radiomics signature and clinical predictors could further improve the prediction ability of PM in patients with OC. • Multisequence MRI-based radiomics showed a favorable discriminatory ability to predict PM in OC. • The nomogram incorporating the radiomics signature and clinical predictors was clinically useful to preoperatively predict PM in patients with OC.

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram

To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set. In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively). The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status. • MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

CT texture analysis in histological classification of epithelial ovarian carcinoma

The study aimed to compare the ability of morphological and texture features derived from contrast-enhanced CT in histological subtyping of epithelial ovarian carcinoma (EOC). Consecutive 205 patients with newly diagnosed EOC who underwent contrast-enhanced CT were included and dichotomised into high-grade serous carcinoma (HGSC) and non-HGSC. Clinical information including age and cancer antigen 125 (CA-125) was documented. The pre-treatment images were analysed using commercial software, TexRAD, by two independent radiologists. Eight qualitative CT morphological features were evaluated, and 36 CT texture features at 6 spatial scale factors (SSFs) were extracted per patient. Features' reduction was based on kappa score, intra-class correlation coefficient (ICC), univariate ROC analysis and Pearson's correlation test. Texture features with ICC ≥ 0.8 were compared by histological subtypes. Patients were randomly divided into training and testing sets by 8:2. Two random forest classifiers were determined and compared: model 1 incorporating selected morphological and clinical features and model 2 incorporating selected texture and clinical features. HGSC showed specifically higher texture features than non-HGSC (p < 0.05). Both models performed highly in predicting histological subtypes of EOC (model 1: AUC 0.891 and model 2: AUC 0.937), and no statistical significance was found between the two models (p = 0.464). CT texture analysis provides objective and quantitative metrics on tumour characteristics with HGSC demonstrating specifically high texture features. The model incorporating texture analysis could classify histology subtypes of EOC with high accuracy and performed as well as morphological features. • A number of CT morphological and texture features showed good inter- and intra-observer agreements. • High-grade serous ovarian carcinoma showed specifically higher CT texture features than non-high-grade serous ovarian carcinoma. • CT texture analysis could differentiate histological subtypes of epithelial ovarian carcinoma with high accuracy.

Comparison of the O-RADS and ADNEX models regarding malignancy rate and validity in evaluating adnexal lesions

This study aimed to compare the ability of the O-RADS and ADNEX models to classify benign or malignant adnexal lesions. This retrospective single-center study included women who underwent surgery for adnexal lesions. Two gynecologists independently categorized the adnexal lesions according to the O-RADS and ADNEX models. Four additional readers were included to validate the new quick-access O-RADS flowchart. Among the 322 patients included in this study, 264 (82.0%) had a benign diagnosis, and 58 (18.0%) had a malignant diagnosis. The malignant rates of O-RADS 2, O-RADS 3, O-RADS 4, and O-RADS 5 were 0%, 3.0%, 37.7%, and 78.9%, respectively. The AUC of the O-RADS in the 322 patients was 0.93. On comparing the O-RADS and ADNEX models in the remaining 281 patients, the AUCs of the O-RADS, ADNEX model with CA125, and ADNEX model without CA125 were 0.92, 0.95, and 0.94, respectively. When setting a uniform cutoff of ≥ 10% (≥ O-RADS 4) to predict malignancy, the O-RADS had higher sensitivity than the ADNEX model (96.6% vs. 91.4%), and relatively similar specificity. In addition, the readers with the quick-access flowchart spent less time categorizing O-RADS than the readers with only the original O-RADS table (mean analysis time: 99 min 15 s vs. 111 min 55 s). The O-RADS classification of the adnexal lesions as benign or malignant was comparable to that of the ADNEX model and had higher sensitivity at the 10% cutoff value. A quick-access O-RADS flowchart was helpful in O-RADS categorization and might shorten the analysis time. • Both O-RADS and ADNEX models had good diagnostic performance in distinguishing adnexal malignancy, and O-RADS had higher sensitivity than ADNEX model in uniform 10% cutoff to predict malignancy. • Quick-access O-RADS flowchart was developed to help review O-RADS classification and might help reduce the analysis time.

Association between MRI histogram features and treatment response in locally advanced cervical cancer treated by chemoradiotherapy

To examine the associations of histogram features of T2-weighted (T2W) images and apparent diffusion coefficient (ADC) with treatment response in locally advanced cervical cancer (LACC) following concurrent chemoradiotherapy (CCRT). Fifty-eight patients who underwent a 4-week CCRT regimen with MRI prior to treatment (pre-CCRT) and after treatment (post-CCRT) were retrospectively analysed. Histogram features were calculated from volumes of interest (VOIs) from one radiologist on T2W images and ADC maps. VOIs from two radiologists were used to assess observer repeatability in delineation and feature values at both time-points with the Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). Treatment response was defined as a 90% reduction in tumour volume. Paired Mann-Whitney U tests were used to determine if features changed significantly between examinations. Two-sample Mann-Whitney U tests were used to identify features that were significantly different between response groups. Receiver operating characteristic (ROC) analysis was done on significantly different MRI features between treatment response groups. Pre-CCRT delineation and feature repeatability were generally good (DSC > 0.700; ICC > 0.750). Post-CCRT repeatability was low (DSC < 0.700; ICC < 0.750), but ADC mean and percentiles retained good ICC scores. All features, except for T2W ADC and T2W histogram features could be used to track changes in LACC tumours undergoing CCRT. Post-CCRT ADC • Pre-treatment tumour delineation and histogram feature values had good observer repeatability, while these were less repeatable at post-treatment. • MRI histogram analysis could be used to track changes in the tumour as it undergoes concurrent chemoradiotherapy. • Post-treatment median ADC was associated with treatment response and had good repeatability.

Diagnostic performance of conventional and advanced imaging modalities for assessing newly diagnosed cervical cancer: systematic review and meta-analysis

To review the diagnostic performance of contemporary imaging modalities for determining local disease extent and nodal metastasis in patients with newly diagnosed cervical cancer. Pubmed and Embase databases were searched for studies published from 2000 to 2019 that used ultrasound (US), CT, MRI, and/or PET for evaluating various aspects of local extent and nodal metastasis in patients with newly diagnosed cervical cancer. Sensitivities and specificities from the studies were meta-analytically pooled using bivariate and hierarchical modeling. Of 1311 studies identified in the search, 115 studies with 13,999 patients were included. MRI was the most extensively studied modality (MRI, CT, US, and PET were evaluated in 78, 12, 9, and 43 studies, respectively). Pooled sensitivities and specificities of MRI for assessing all aspects of local extent ranged between 0.71-0.88 and 0.86-0.95, respectively. In assessing parametrial invasion (PMI), US demonstrated pooled sensitivity and specificity of 0.67 and 0.94, respectively-performance levels comparable with those found for MRI. MRI, CT, and PET performed comparably for assessing nodal metastasis, with low sensitivity (0.29-0.69) but high specificity (0.88-0.98), even when stratified to anatomical location (pelvic or paraaortic) and level of analysis (per patient vs. per site). MRI is the method of choice for assessing any aspect of local extent, but where not available, US could be of value, particularly for assessing PMI. CT, MRI, and PET all have high specificity but poor sensitivity for the detection of lymph node metastases. • Magnetic resonance imaging is the method of choice for assessing local extent. • Ultrasound may be helpful in determining parametrial invasion, especially in lower-resourced countries. • Computed tomography, magnetic resonance imaging, and positron emission tomography perform similarly for assessing lymph node metastasis, with high specificity but low sensitivity.

MRI texture features differentiate clinicopathological characteristics of cervical carcinoma

To evaluate MRI texture analysis in differentiating clinicopathological characteristics of cervical carcinoma (CC). Patients with newly diagnosed CC who underwent pre-treatment MRI were retrospectively reviewed. Texture analysis was performed using commercial software (TexRAD). Largest single-slice ROIs were manually drawn around the tumour on T2-weighted (T2W) images, apparent diffusion coefficient (ADC) maps and contrast-enhanced T1-weighted (T1c) images. First-order texture features were calculated and compared among histological subtypes, tumour grades, FIGO stages and nodal status using the Mann-Whitney U test. Feature selection was achieved by elastic net. Selected features from different sequences were used to build the multivariable support vector machine (SVM) models and the performances were assessed by ROC curves and AUC. Ninety-five patients with FIGO stage IB~IVB were evaluated. A number of texture features from multiple sequences were significantly different among all the clinicopathological subgroups (p < 0.05). Texture features from different sequences were selected to build the SVM models. The AUCs of SVM models for discriminating histological subtypes, tumour grades, FIGO stages and nodal status were 0.841, 0.850, 0.898 and 0.879, respectively. Texture features derived from multiple sequences were helpful in differentiating the clinicopathological signatures of CC. The SVM models with selected features from different sequences offered excellent diagnostic discrimination of the tumour characteristics in CC. • First-order texture features are able to differentiate clinicopathological signatures of cervical carcinoma. • Combined texture features from different sequences can offer excellent diagnostic discrimination of the tumour characteristics in cervical carcinoma.

Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images

To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images. One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann-Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models. A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71-0.88) in the training cohort and 0.77 (95% CI, 0.65-0.88) in the validation cohort. The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC. • Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment. • The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively. • The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.

Value of integrated PET-IVIM MR in assessing metastases in hypermetabolic pelvic lymph nodes in cervical cancer: a multi-parameter study

To evaluate the value of integrated multi-parameter positron emission tomography-intravoxel incoherent motion magnetic resonance (PET-IVIM MR) imaging for pelvic lymph nodes with high FDG uptake in cervical cancer, and to determine the best combination of parameters. A total of 38 patients with 59 lymph nodes with high FDG uptake were included. The imaging parameters of the lymph nodes were calculated by PET-IVIM MR, and the differences between lymph nodes diagnosed by postoperative pathology as metastasis versus non-metastasis were compared. We used the receiver operating characteristic (ROC) curve and logistic regression to construct a combination prediction model to filter low value and similar parameters, in order to search the optimal combination of PET/MR parameters for predicting pathologically confirmed metastatic lymph nodes. The correlation between diffusion parameters and metabolic parameters was analyzed by Spearman's rank correlation. The maximum standardized uptake value (SUV The combination of MTV, SUV • Integrated PET-IVIM MR may assist to characterize lymph node status. • The combination of MTV, SUV

Pelvic insufficiency fracture or bone metastasis after radiotherapy for cervical cancer? The added value of DWI for characterization

We sought to determine the added value of diffusion-weighted magnetic resonance imaging (DWI) in the differentiation of pelvic insufficiency fracture (PIF) from bone metastasis after radiotherapy in cervical cancer patients. In the present study, 42 cervical cancer patients after radiotherapy with 61 bone lesions (n = 40, PIFs; n = 21, bone metastasis) were included. Conventional MRI and DWI were performed in all patients. For qualitative imaging diagnosis, two sets of images were reviewed independently by three observers, including a conventional MRI set (unenhanced T1-weighted, T2-weighted, and enhanced T1-weighted images) and a DWI set (conventional MRIs, DW images, and ADC maps). The mean ADC value of each lesson was measured on ADC maps. The diagnostic performance was assessed by using the area under the receiver operating characteristic curve (A For all observers, the A The addition of DWI to conventional MRI improved the differentiation of PIF from bone metastasis after RT in patients with cervical cancer. • DWI showed additive value to conventional MRI in the differentiation of PIF from bone metastasis after RT. • For qualitative diagnosis, the addition of DWI can improve diagnostic performance compared with conventional MRI alone and can particularly improve the sensitivity. • Quantitative ADC assessment showed potential value for identifying PIF from bone metastasis.

Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer

To develop and evaluate the performance of U-Net for fully automated localization and segmentation of cervical tumors in magnetic resonance (MR) images and the robustness of extracting apparent diffusion coefficient (ADC) radiomics features. This retrospective study involved analysis of MR images from 169 patients with cervical cancer stage IB-IVA captured; among them, diffusion-weighted (DW) images from 144 patients were used for training, and another 25 patients were recruited for testing. A U-Net convolutional network was developed to perform automated tumor segmentation. The manually delineated tumor region was used as the ground truth for comparison. Segmentation performance was assessed for various combinations of input sources for training. ADC radiomics were extracted and assessed using Pearson correlation. The reproducibility of the training was also assessed. Combining b0, b1000, and ADC images as a triple-channel input exhibited the highest learning efficacy in the training phase and had the highest accuracy in the testing dataset, with a dice coefficient of 0.82, sensitivity 0.89, and a positive predicted value 0.92. The first-order ADC radiomics parameters were significantly correlated between the manually contoured and fully automated segmentation methods (p < 0.05). Reproducibility between the first and second training iterations was high for the first-order radiomics parameters (intraclass correlation coefficient = 0.70-0.99). U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. First-order radiomics features extracted from whole tumor volume demonstrate the potential robustness for longitudinal monitoring of tumor responses in broad clinical settings. U-Net-based deep learning can perform accurate localization and segmentation of cervical cancer in DW MR images. • U-Net-based deep learning can perform accurate fully automated localization and segmentation of cervical cancer in diffusion-weighted MR images. • Combining b0, b1000, and apparent diffusion coefficient (ADC) images exhibited the highest accuracy in fully automated localization. • First-order radiomics feature extraction from whole tumor volume was robust and could thus potentially be used for longitudinal monitoring of treatment responses.

Multiparametric PET/MR (PET and MR-IVIM) for the evaluation of early treatment response and prediction of tumor recurrence in patients with locally advanced cervical cancer

To assess the value of Fifty-one patients with LACC underwent pelvic PET/MR scans with an IVIM sequence at two time-points (pretreatment [pre] and midtreatment [mid]). Pre- and mid-PET parameters (SUV Thirty-two patients were classified into the good response (GR) group with TESR ≥ 50%, and 19 patients were categorized into the poor response (PR) group with TESR < 50%. Δ%D (p = 0.013) and Δ%F (p = 0.006) are independently related to TESR with superior combined diagnostic ability (AUC = 0.901). Pre-TLG, Δ%D, and suspicious lymph node metastasis (SLNM) were selected for the construction of the combined prediction model. The model for identifying the patients with high risk of tumor recurrence reached a moderate predictive ability and good stability with c-index of 0.764 (95% CI, 0.672-0.855). The combined prediction model based on pretreatment PET metabolic parameter (pre-TLG), IVIM-D percentage changes, and LNs status provides great potential to identify the LACC patients with high risk of recurrence at early stage of CCRT. • PET/MR plus IVIM offers various complementary information for LACC. • IVIM-D and IVIM-F percentage changes are independently related to tumor early shrinkage rates. • The combined prediction model can help identify the LACC patients with high risk of tumor recurrence.

Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018

The recommendations cover indications for MRI examination including acquisition planes, patient preparation, imaging protocol including multi-parametric approaches such as diffusion-weighted imaging (DWI-MR),  dynamic contrast-enhanced imaging (DCE-MR) and standardised reporting. The document also underscores the value of whole-body 18-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG-PET/CT) and highlights potential future methods. In 2019, the ESUR female pelvic imaging working group reviewed the revised 2018 FIGO staging system, the up-to-date clinical management guidelines, and the recent imaging literature. The RAND-UCLA Appropriateness Method (RAM) was followed to develop the current ESUR consensus guidelines following methodological steps: literature research, questionnaire developments, panel selection, survey, data extraction and analysis. The updated ESUR guidelines are recommendations based on ≥ 80% consensus among experts. If ≥ 80% agreement was not reached, the action was indicated as optional. The present ESUR guidelines focus on the main role of MRI in the initial staging, response monitoring and evaluation of disease recurrence. Whole-body FDG-PET plays an important role in the detection of lymph nodes (LNs) and distant metastases. • T2WI and DWI-MR are now recommended for initial staging, monitoring of response and evaluation of recurrence. • DCE-MR is optional; its primary role remains in the research setting. • T2WI, DWI-MRI and whole-body FDG-PET/CT enable comprehensive assessment of treatment response and recurrence.

Added-value of dynamic contrast-enhanced MRI on prediction of tumor recurrence in locally advanced cervical cancer treated with chemoradiotherapy

To evaluate whether the DCE-MRI derived parameters integrated into clinical and conventional imaging variables may improve the prediction of tumor recurrence for locally advanced cervical cancer (LACC) patients following concurrent chemoradiotherapy (CCRT). Between March 2014 and November 2019, 79 consecutive LACC patients who underwent pelvic MRI examinations with DCE-MRI sequence before treatment were prospectively enrolled. The primary outcome was disease-free survival (DFS). DCE-MRI derived parameters, conventional imaging, and clinical factors were collected. Univariate and multivariate Cox hazard regression analyses were performed to evaluate these parameters in the prediction of DFS. The independent and prognostic interested variables were combined to build a prediction model compared with the clinical International Federation of Gynecological (FIGO) staging system. Lymph node metastasis (LNM) and the mean value of v The v Lower v

Association between IVIM parameters and treatment response in locally advanced squamous cell cervical cancer treated by chemoradiotherapy

To examine the associations of intravoxel incoherent motion (IVIM) parameters with treatment response in cervical cancer following concurrent chemoradiotherapy (CCRT). Forty-five patients, median age of 58 years (range: 28-82), with pre-CCRT and post-CCRT MRI, were retrospectively analysed. The IVIM parameters pure diffusion coefficient (D) and perfusion fraction (f) were estimated using the full b-value distribution (BVD) as well as an optimised subsample BVD. Dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC) were used to measure observer repeatability in tumour delineation at both time points. Treatment response was determined by the response evaluation criteria in solid tumour (RECIST) 1.1 between MRI examinations. Mann-Whitney U tests were used to test for significant differences in IVIM parameters between treatment response groups. Pre-CCRT tumour delineation repeatability was good (DSC = 0.81) while post-CCRT delineation repeatability was moderate (DSC = 0.67). Values of D and f had good repeatability at both time points (ICC > 0.80). Pre-CCRT f estimated using the full BVD and optimised subsample BVD were found to be significantly higher in patients with partial response compared to those with stable disease or disease progression (p = 0.01 and 95% CI = -0.02-0.00 for both cases). Pre-CCRT f was associated with treatment response in cervical cancer with good observer repeatability. Similar discriminative ability was also observed in estimated pre-CCRT f from an optimised subsample BVD. • Pre-treatment tumour delineation and IVIM parameters had good observer repeatability. • Post-treatment tumour delineation was worse than at pre-treatment, but IVIM parameters retained good ICC. • Pre-treatment perfusion fraction estimated from all b-values and an optimised subsample of b-values were associated with treatment response.

Multiparametric magnetic resonance imaging-derived radiomics for the prediction of disease-free survival in early-stage squamous cervical cancer

To conduct multiparametric magnetic resonance imaging (MRI)-derived radiomics based on multi-scale tumor region for predicting disease-free survival (DFS) in early-stage squamous cervical cancer (ESSCC). A total of 191 ESSCC patients (training cohort, n = 135; validation cohort, n = 56) from March 2016 to September 2019 were retrospectively recruited. Radiomics features were derived from the T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CET1WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) map for each patient. DFS-related radiomics features were selected in 3 target tumor volumes (VOI Three radiomics scores all showed favorable performances in DFS prediction. Rad-score (VOI Multiparametric MRI-derived radiomics based on multi-scale tumor region can aid in the prediction of DFS for ESSCC patients, thereby facilitating clinical decision-making. • Three radiomics scores based on multi-scale tumor region all showed favorable performances in DFS prediction. Rad-score (VOI

Whole-tumor texture model based on diffusion kurtosis imaging for assessing cervical cancer: a preliminary study

To evaluate the diagnostic potential of diffusion kurtosis imaging (DKI) functional maps with whole-tumor texture analysis in differentiating cervical cancer (CC) subtype and grade. Seventy-six patients with CC were enrolled. First-order texture features of the whole tumor were extracted from DKI and DWI functional maps, including apparent kurtosis coefficient averaged over all directions (MK), kurtosis along the axial direction (Ka), kurtosis along the radial direction (Kr), mean diffusivity (MD), fractional anisotropy (FA), and ADC maps, respectively. The Mann-Whitney U test and ROC curve were used to select the most representative texture features. Models based on each individual and combined functional maps were established using multivariate logistic regression analysis. Conventional parameters-the average values of ADC and DKI parameters derived from the conventional ROI method-were also evaluated. The combined model based on Ka, Kr, MD, and FA maps yielded the best diagnostic performance in discrimination of cervical squamous cell cancer (SCC) and cervical adenocarcinoma (CAC) with the highest AUC (0.932). Among individual functional map derived models, Kr map-derived model showed the best performance when differentiating tumor subtypes (AUC = 0.828). MK_90th percentile was useful for distinguishing high-grade and low-grade in SCC tumors with an AUC of 0.701. The average values of MD, FA, and ADC were significantly different between SCC and CAC, but no conventional parameters were useful for tumor grading. The whole-tumor texture analysis applied to DKI functional maps can be used for differential diagnosis of cervical cancer subtypes and grading SCC. • The whole-tumor texture analysis applied to DKI functional maps allows accurate differential diagnosis of CC subtype and grade. • The combined model derived from multiple functional maps performs significantly better than the single models when differentiating tumor subtypes. • MK_90th percentile was useful for distinguishing poorly and well-/moderately differentiated SCC tumors with an AUC of 0.701.

PET-CT radiomics by integrating primary tumor and peritumoral areas predicts E-cadherin expression and correlates with pelvic lymph node metastasis in early-stage cervical cancer

To explore the role of radiomics in integrating primary tumor and peritumoral areas based on PET-CT scans for predicting E-cadherin (E-cad) expression in early-stage cervical cancer (ESCC) and its correlation with pelvic lymph node metastasis (PLNM). Ninety-seven ESCC patients who had undergone PET-CT scans were retrospectively analyzed. The ROI of primary tumors, peritumoral areas, and plus tumors were semi-automatically segmented on PET-CT images. A total of 1188 radiomics features were extracted, selected, and eventually integrated into radiomics score (rad-score). The rad-score difference between patients with E-cad expression of high and low was analyzed using Mann-Whitney tests. Characteristic correlation was tested using a Spearman analysis. Four models were established using logistic regression algorithms and evaluated using ROC and calibration curves. A DeLong test was used to perform pairwise comparisons of AUCs. The rad-score of patients with low E-cad expression was higher than that of patients with high E-cad expression in both training and testing cohorts (p < 0.001 and p = 0.027, respectively). A significant correlation was observed between the rad-score and E-cad (p < 0.001). PLNM correlated slightly with rad-score and E-cad values (p = 0.01 and p < 0.001, respectively). The ROC curve and calibration curve of the rad-score model performed best in both training and testing cohorts (AUC = 0.915, 0.844, p < 0.001, respectively). The radiomics of integrating primary tumor and peritumoral areas based on PET-CT showed correlations with PLNM. It was also able to predict E-cad expression in ESCC patients, allowing for evaluation of those patients' prognosis and more individualized medical treatment. • By integrating the primary tumor and peritumoral area based on PET-CT, radiomics was feasible. • The rad-score was associated with E-cad expression and PLNM in patients with ESCC. • Radiomics that integrated the primary tumor and peritumoral areas based on PET-CT could predict E-cad expression in patients with ESCC.

MRI-derived radiomics analysis improves the noninvasive pretreatment identification of multimodality therapy candidates with early-stage cervical cancer

To develop and validate a clinical-radiomics model that incorporates radiomics signatures and pretreatment clinicopathological parameters to identify multimodality therapy candidates among patients with early-stage cervical cancer. Between January 2017 and February 2021, 235 patients with IB1-IIA1 cervical cancer who underwent radical hysterectomy were enrolled and divided into training (n = 194, training:validation = 8:2) and testing (n = 41) sets according to surgical time. The radiomics features of each patient were extracted from preoperative sagittal T2-weighted images. Significance testing, Pearson correlation analysis, and Least Absolute Shrinkage and Selection Operator were used to select radiomic features associated with multimodality therapy administration. A clinical-radiomics model incorporating radiomics signature, age, 2018 Federation International of Gynecology and Obstetrics (FIGO) stage, menopausal status, and preoperative biopsy histological type was developed to identify multimodality therapy candidates. A clinical model and a clinical-conventional radiological model were also constructed. A nomogram and decision curve analysis were developed to facilitate clinical application. The clinical-radiomics model showed good predictive performance, with an area under the curve, sensitivity, and specificity in the testing set of 0.885 (95% confidence interval: 0.781-0.989), 78.9%, and 81.8%, respectively. The AUC, sensitivity, and specificity of the clinical model and clinical-conventional radiological model were 0.751 (0.603-0.900), 63.2%, and 63.6%, 0.801 (0.661-0.942), 73.7%, and 68.2%, respectively. A decision curve analysis demonstrated that when the threshold probability was > 20%, the clinical-radiomics model or nomogram may be more advantageous than the treat all or treat-none strategy. The clinical-radiomics model and nomogram can potentially identify multimodality therapy candidates in patients with early-stage cervical cancer. • Pretreatment identification of multimodality therapy candidates among patients with early-stage cervical cancer helped to select the optimal primary treatment and reduce severe complication risk and costs. • The clinical-radiomics model achieved a better prediction performance compared with the clinical model and the clinical-conventional radiological model. • An easy-to-use nomogram exhibited good performance for individual preoperative prediction.

Ovarian-Adnexal Imaging-Reporting and Data System (O-RADS) ultrasound version 2019: a prospective validation and comparison to updated version (v2022) in pathologically confirmed adnexal masses

To evaluate the diagnostic accuracy and reliability of the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound v2019 in classifying adnexal masses (AMs) and compare the old and updated systems (v2022). This prospective study enrolled 977 consecutive women with suspected AMs from three institutions between January 2022 and December 2023. Ultrasound examinations were performed by three experienced radiologists who categorized AMs according to O-RADS ultrasound v2019. The same radiologists retrospectively reviewed the stored ultrasound images and provided the O-RADS ultrasound v2022 classification. Histopathology was used as the reference standard to calculate the diagnostic accuracy of the O-RADS versions in predicting malignant AMs. Inter-observer agreement (IOA) of the O-RADS scoring results was evaluated using the Fleiss kappa (κ) test. The final analysis included 803 women with 855 AMs (219 (25.6%) malignant and 636 (74.4%) benign). Both O-RADS versions demonstrated good diagnostic accuracy, with area under the curve (AUC) values ranging from 0.906 to 0.923 (v2019) and 0.919 to 0.936 (v2022). The updated v2022 showed a slightly higher accuracy (82.5-86.7% vs. 80.7-85.3%), sensitivity (93.6-95.0% vs. 92.2-94.1%), and specificity (78.1-84.1% vs. 76.1-82.9%) compared to v2019. The IOA for the overall O-RADS classification was perfect for both versions (κ = 0.96-0.97). The O-RADS ultrasound classification system demonstrated good diagnostic accuracy and reliability in predicting malignant AMs, with the updated v2022 showing modest improvements. Question Accurate classification of adnexal masses is essential for management. Can updated O-RADS ultrasound v2022 improve diagnostic accuracy and reliability compared to v2019 in predicting malignancies? Findings O-RADS ultrasound v2022 demonstrated slightly higher diagnostic accuracy for identifying malignant adnexal masses compared to v2019, reflecting modest improvements in risk stratification and clinical decision-making. Clinical relevance The updated O-RADS ultrasound v2022 provides improved risk stratification for adnexal masses, enhancing diagnostic confidence, supporting more precise clinical decision-making, and improving patient outcomes through timely intervention or tailored management strategies in ovarian cancer care.

Performance of grayscale combined with contrast-enhanced ultrasound in differentiating benign and malignant pediatric ovarian masses

To evaluate grayscale US combined with contrast-enhanced ultrasound (CEUS) in the preoperative differentiation between benign and malignant ovarian masses in a pediatric population. This retrospective study enrolled patients who underwent grayscale US and CEUS before surgery because of ovarian masses between July 2018 and September 2023, with available histopathologic or follow-up results. Two senior radiologists summarized the grayscale US and CEUS characteristics of all ovarian masses, including percentage of solidity, ascites, vascularity, and enhanced vessel morphology. These characteristics were then independently reviewed by radiologists with different experience to assess interobserver agreement. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC), while interobserver agreement was evaluated by intraclass correlation coefficient (ICC). A total of 26 children (median age: 10.1 [7.5, 11.7] years; age range: 0-14 years; benign: 15 patients) were included. The main characteristics of malignant ovarian tumors were abundant blood flow and twisted blood vessels within the mass, enhanced portion of the mass over 50 percent (all p < 0.001). The grayscale US combined with CEUS showed better diagnostic performance than the grayscale US alone (AUC = 0.99 [95% CI: 0.95, 1.00] vs AUC = 0.70 [95% CI: 0.50, 0.90] p < 0.001). A statistically significant AUC before and after CEUS was also shown between two junior radiologists (0.75 vs 0.92 and 0.69 vs 0.86, respectively, both p < 0.05). ICC of CEUS was better than that of grayscale US among radiologists. The combination of grayscale US and CEUS might improve the diagnostic accuracy in differentiating benign and malignant pediatric ovarian masses. Grayscale ultrasound combined with contrast-enhanced ultrasound can improve the diagnostic performance in the preoperative differentiation of benign and malignant ovarian lesions in a pediatric population. Correctly distinguishing benign from malignant ovarian masses in pediatric patients is critical for determining treatments. Grayscale combined with contrast-enhanced ultrasound (CEUS) differentiated benign and malignant pediatric ovarian masses better than grayscale US alone. Junior radiologists' diagnostic performances could be and were significantly improved with the application of CEUS.

Updated ESUR Guidelines for Endometrial Cancer: integrating MRI with the 2023 FIGO Staging Revolution

To summarize the key updates introduced in the 2023 International Federation of Gynecology and Obstetrics (FIGO) classification for endometrial cancer (EC), and to highlight the role of MRI in aligning with these changes for improved staging and patient management. A review of the updated 2023 FIGO classification, which integrates molecular profiling and histopathological criteria, was conducted. Additionally, the revised European Society of Urogenital Radiology (ESUR) MRI recommendations were analyzed to assess their alignment with the new FIGO framework, focusing on their role in evaluating myometrial invasion (MI) and cervical stromal involvement. The updated FIGO classification incorporates molecular data to refine risk stratification and staging accuracy. MRI continues to play a pivotal role in distinguishing between stages, mapping disease extent, and guiding surgical planning. The updated ESUR recommendations emphasize standardized MRI protocols, particularly the use of multiphase contrast-enhanced imaging, to improve diagnostic confidence in assessing MI. The integration of molecular classification into FIGO staging, supported by standardized and advanced MRI protocols as recommended by ESUR, enhances the management of endometrial cancer. Question The 2023 FIGO update integrates molecular profiling into endometrial cancer staging, requiring MRI adaptations to improve accuracy in assessing disease extent, including myometrial invasion. Findings Updated ESUR MRI guidelines emphasize multiphase contrast-enhanced imaging, structured reporting, and integration with FIGO 2023 classification, enhancing diagnostic precision for staging and treatment planning. Clinical relevance Standardized MRI protocols aligned with FIGO 2023 system improve endometrial cancer staging, guiding optimal surgical and therapeutic strategies, reducing diagnostic variability, and enhancing patient outcomes through individualized risk stratification and personalized treatment.

European Society of Urogenital Radiology (ESUR) guidelines on MR imaging prior to fertility-sparing treatments in patients with cervical, endometrial, and ovarian cancers

To establish standardised MRI protocols and structured reporting guidelines for optimal patient selection in fertility-sparing treatments for gynaecological cancers. The European Society of Urogenital Radiology (ESUR) Female Pelvis Working Group utilised the RAND-UCLA Appropriateness Method to develop these guidelines. A multidisciplinary panel composed of ten radiologists and two gynaecological oncologists conducted a comprehensive review of clinical and imaging literature (until 28th February 2025) and evaluated MRI protocols through a structured survey consisting of 104 questions across five sections covering MR imaging preparation, equipment specifications, protocols, and reporting standards. Recommendations achieving ≥ 80% consensus were designated as "RECOMMENDED", with those below this threshold marked as "SUGGESTED" or "NOT RECOMMENDED". Consensus was reached on MRI technical requirements, including sequence selection, imaging planes, and contrast timing. Disease-specific structured reporting templates were developed with standardised criteria for cervical, endometrial and ovarian cancers. These evidence-based guidelines provide a standardised framework for MRI acquisition and reporting to support optimal patient selection for fertility-sparing treatments. By harmonising imaging protocols and structured reporting, we aim to enhance diagnostic accuracy and clinical decision-making. These guidelines represent a key step toward developing comprehensive recommendations for fertility preservation, with future validation and adaptation ensuring their applicability across diverse clinical settings. Question Fertility-sparing treatments in gynaecological cancers require adherence to strict selection criteria based on tumour stage, size, and histological subtype. Findings MRI is essential for accurately assessing eligibility criteria in cervical and endometrial cancers, and characterising adnexal masses using standardised reporting criteria. Clinical relevance MRI is valuable for the preoperative evaluation of patients considered for fertility-sparing treatments in gynaecologic cancers. Key parameters include precise tumour measurements, depth of invasion, and local tumour extent through optimised protocols combining anatomical and functional sequences.

Evaluating the quality of radiomics-based studies for endometrial cancer using RQS and METRICS tools

Abstract Objective To assess the methodological quality of radiomics-based models in endometrial cancer using the radiomics quality score (RQS) and METhodological radiomICs score (METRICS). Methods We systematically reviewed studies published by October 30th, 2023. Inclusion criteria were original radiomics studies on endometrial cancer using CT, MRI, PET, or ultrasound. Articles underwent a quality assessment by novice and expert radiologists using RQS and METRICS. The inter-rater reliability for RQS and METRICS among radiologists with varying expertise was determined. Subgroup analyses were performed to assess whether scores varied according to study topic, imaging technique, publication year, and journal quartile. Results Sixty-eight studies were analysed, with a median RQS of 11 (IQR, 9–14) and METRICS score of 67.6% (IQR, 58.8–76.0); two different articles reached maximum RQS of 19 and METRICS of 90.7%, respectively. Most studies utilised MRI (82.3%) and machine learning methods (88.2%). Characterisation and recurrence risk stratification were the most explored outcomes, featured in 35.3% and 19.1% of articles, respectively. High inter-rater reliability was observed for both RQS (ICC: 0.897; 95% CI: 0.821, 0.946) and METRICS (ICC: 0.959; 95% CI: 0.928, 0.979). Methodological limitations such as lack of external validation suggest areas for improvement. At subgroup analyses, no statistically significant difference was noted. Conclusions Whilst using RQS, the quality of endometrial cancer radiomics research was apparently unsatisfactory, METRICS depicts a good overall quality. Our study highlights the need for strict compliance with quality metrics. Adhering to these quality measures can increase the consistency of radiomics towards clinical application in the pre-operative management of endometrial cancer. Clinical relevance statement Both the RQS and METRICS can function as instrumental tools for identifying different methodological deficiencies in endometrial cancer radiomics research. However, METRICS also reflected a focus on the practical applicability and clarity of documentation. Key Points The topic of radiomics currently lacks standardisation, limiting clinical implementation. METRICS scores were generally higher than the RQS, reflecting differences in the development process and methodological content. A positive trend in METRICS score may suggest growing attention to methodological aspects in radiomics research.

Nodal infiltration in endometrial cancer: a prediction model using best subset regression

To build preoperative prediction models with and without MRI for regional lymph node metastasis (r-LNM, pelvic and/or para-aortic LNM (PENM/PANM)) and for PANM in endometrial cancer using established risk factors. In this retrospective two-center study, 364 patients with endometrial cancer were included: 253 in the model development and 111 in the external validation. For r-LNM and PANM, respectively, best subset regression with ten-time fivefold cross validation was conducted using ten established risk factors (4 clinical and 6 imaging factors). Models with the top 10 percentile of area under the curve (AUC) and with the fewest variables in the model development were subjected to the external validation (11 and 4 candidates, respectively, for r-LNM and PANM). Then, the models with the highest AUC were selected as the final models. Models without MRI findings were developed similarly, assuming the cases where MRI was not available. The final r-LNM model consisted of pelvic lymph node (PEN) ≥ 6 mm, deep myometrial invasion (DMI) on MRI, CA125, para-aortic lymph node (PAN) ≥ 6 mm, and biopsy; PANM model consisted of DMI, PAN, PEN, and CA125 (in order of correlation coefficient β values). The AUCs were 0.85 (95%CI: 0.77-0.92) and 0.86 (0.75-0.94) for the external validation, respectively. The model without MRI for r-LNM and PANM showed AUC of 0.79 (0.68-0.89) and 0.87 (0.76-0.96), respectively. The prediction models created by best subset regression with cross validation showed high diagnostic performance for predicting LNM in endometrial cancer, which may avoid unnecessary lymphadenectomies. The prediction risks of lymph node metastasis (LNM) and para-aortic LNM can be easily obtained for all patients with endometrial cancer by inputting the conventional clinical information into our models. They help in the decision-making for optimal lymphadenectomy and personalized treatment. •Diagnostic performance of lymph node metastases (LNM) in endometrial cancer is low based on size criteria and can be improved by combining with other clinical information. •The optimized logistic regression model for regional LNM consists of lymph node ≥ 6 mm, deep myometrial invasion, cancer antigen-125, and biopsy, showing high diagnostic performance. •Our model predicts the preoperative risk of LNM, which may avoid unnecessary lymphadenectomies.

A comparative analysis of MRI findings in endometrial cancer: differentiation between endometrioid adenocarcinoma, serous carcinoma, and clear cell carcinoma

To assess the magnetic resonance imaging (MRI) findings of endometrial cancers and to reveal the differences between endometrioid carcinoma (EC), serous carcinoma (SC), and clear cell carcinoma (CCC). In this study, 274 consecutive patients with histopathologically confirmed endometrial cancer (231 ECs, 25 SCs, and 18 CCCs) who underwent MRI before hysterectomy were enrolled. MRI images were retrospectively reviewed and compared between the three pathologies. The maximum diameters (55.6 ± 34.7 vs. 39.3 ± 21.6 vs. 39.4 ± 26.8 mm) (p < 0.05) and apparent diffusion coefficient (ADC) values (1.11 ± 0.21 vs. 0.84 ± 0.17 vs. 0.86 ± 0.16 × 10 SCs frequently exhibited a heterogeneous signal with peritoneal dissemination and abnormal ascites. Alternatively, CCCs tended to have a larger tumor size and higher ADC values with an infiltrative growth pattern, heterogeneous signal, and abnormal ascites. • SCs tend to have a heterogeneous signal intensity with peritoneal dissemination and abnormal ascites compared to ECs. • CCCs tend to have a heterogeneous signal intensity with an infiltrative growth pattern and abnormal ascites compared to ECs. • CCCs have a larger tumor size and higher ADC values compared to ECs and SCs.

Volumetric ADC histogram analysis for preoperative evaluation of LVSI status in stage I endometrioid adenocarcinoma

To investigate the value of volumetric ADC histogram metrics for evaluating lymphovascular space invasion (LVSI) status in stage I endometrioid adenocarcinoma (EAC). Preoperative MRI of 227 patients with stage I EAC were retrospectively analyzed. ADC histogram data were derived from the whole tumor with ROIs drawn on all slices of DWI scans (b = 0, 1000 s/mm The minADC and meanADC and 10th, 25th, 50th, 75th, and 90th percentiles of ADC were significantly lower in LVSI-positive EACs compared with those in the LVSI-negative groups for stage I, Ia, and Ib EACs (all p < 0.05). MeanADC ≤ 0.857 × 10 Volumetric ADC histogram metrics might be helpful for the preoperative evaluation of LVSI status and personalized clinical management in patients with stage I EAC. • Volumetric ADC histogram analysis helps evaluate LVSI status preoperatively. • LVSI-positive EAC is associated with a reduction in multiple volumetric ADC histogram metrics. • MeanADC and the 90th percentile of ADC were shown to be best in evaluating LVSI- positivity in stage Ia and Ib EACs, respectively.

Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study

To construct a MRI radiomics model and help radiologists to improve the assessments of pelvic lymph node metastasis (PLNM) in endometrial cancer (EC) preoperatively. During January 2014 and May 2019, 622 EC patients (age 56.6 ± 8.8 years; range 27-85 years) from five different centers (A to E) were divided into training set, validation set 1 (351 cases from center A), and validation set 2 (271 cases from centers B-E). The radiomics features were extracted basing on T2WI, DWI, ADC, and CE-T1WI images, and most related radiomics features were selected using the random forest classifier to build a radiomics model. The ROC curve was used to evaluate the performance of training set and validation sets, radiologists based on MRI findings alone, and with the aid of the radiomics model. The clinical decisive curve (CDC), net reclassification index (NRI), and total integrated discrimination index (IDI) were used to assess the clinical benefit of using the radiomics model. The AUC values were 0.935 for the training set, 0.909 and 0.885 for validation sets 1 and 2, 0.623 and 0.643 for the radiologists 1 and 2 alone, and 0.814 and 0.842 for the radiomics-aided radiologists 1 and 2, respectively. The AUC, CDC, NRI, and IDI showed higher diagnostic performance and clinical net benefits for the radiomics-aided radiologists than for the radiologists alone. The MRI-based radiomics model could be used to assess the status of pelvic lymph node and help radiologists improve their performance in predicting PLNM in EC. • A total of 358 radiomics features were extracted. The 37 most important features were selected using the random forest classifier. • The reclassification measures of discrimination confirmed that the radiomics-aided radiologists performed better than the radiologists alone, with an NRI of 1.26 and an IDI of 0.21 for radiologist 1 and an NRI of 1.37 and an IDI of 0.24 for radiologist 2.

Gaussian mixture model-based cluster analysis of apparent diffusion coefficient values: a novel approach to evaluate uterine endometrioid carcinoma grade

The purpose of our study was to perform Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma, and to evaluate the relationship between histological grade and the ratios of the different clusters in each patient. This retrospective study enrolled 122 patients (training: n = 63; and validation: n = 59) imaged between May 2015 and February 2020. In the training cohort, manual segmentation was performed on the ADC maps to obtain the ADC data of each patient, and these ADC data were summated to obtain the "All-patient" ADC data. Cluster analysis (three clusters) was performed on this All-patient ADC data, and the ADC ranges of each cluster were defined as follows: cluster 1, 490-699 × 10 In the training cohort, a significant positive correlation was found between the cluster 1 ratio and histological grade (ρ = 0.34, p = 0.0059). The cluster 1 ratios of high-grade lesions (grade 3) were significantly higher than those of low-grade lesions (grades 1 and 2) (p = 0.0084). A similar significant positive correlation was found between the cluster 1 ratio and histological grade in the validation cohort (ρ = 0.44, p = 0.0006). The cluster 1 ratio containing voxels with low ADC was significantly correlated with the histological grade of endometrioid carcinoma. • We performed Gaussian mixture model (GMM)-based cluster analysis of the apparent diffusion coefficient (ADC) data of patients with endometrioid carcinoma. • The cluster 1 ratio, which included low ADC values, was significantly positive correlated with histological grade in the training and validation cohorts. • The GMM-based cluster analysis of voxel-based ADC data was effective for grading endometrioid carcinoma.

Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection

To investigate the associations between apparent diffusion coefficient (ADC) values extracted from three different region of interest (ROI) position approaches and programmed cell death ligand-1 (PD-L1) expression, and evaluate the performance of the nomogram established based on ADC values and clinicopathological parameters in predicting PD-L1 expression in cervical cancer (CC) patients. Through retrospective recruitment, a training cohort of 683 CC patients was created, and a validation cohort of 332 CC patients was prospectively recruited. ROIs were delineated using three different methods to measure the mean ADC (ADC The ADC ADC The minimum ADC can serve as a reliable imaging biomarker related to PD-L1 expression; the established nomogram combines the minimum ADC and clinicopathological factors that can assist clinical immunotherapy decisions.

Greenhouse gas emissions due to long-term data storage of CT with reformats and strategies for mitigation

Abstract Objectives Medical image data storage and associated greenhouse gas (GHG) emissions are increasing. We aimed to measure non-essential storage and model mitigation strategies. Materials and methods The proportion of stored post-processed series (reformats and reconstructions) was retrospectively recorded in 183 baseline staging CT chest–abdomen–pelvis studies (CT-CAP) for endometrial cancer in a UK referral centre between 2013 and 2016 (Cohort A). File size (megabytes, MB) of each series was recorded for 30 studies (Cohort B) and compared with 100 Canadian studies (Cohort C), contextualised by a survey of protocols across 17 global centres (including Cohort C). Storage-associated GHG emissions were modelled over 20 years for various mitigation strategies. Results Post-processed series were stored in 179/183 (97%) of cohort A, 29/30 (97%) of cohort B and 16/17 (94%) of global centres. Median file size was 787 MB (IQR 460, 1257) for the entire CT study (all stored series) and 290 MB (224, 355) for the acquired axial series alone. On-premises storage of all series for new UK endometrial cancer baseline studies 2020–2040 is estimated to generate 381 metric tons CO 2 equivalent (MTCO 2 e). Over this period, modelled mitigation strategies achieved emission reductions of 69% by storing only acquired axial series (117MTCO 2 e), 82% combining axial-only with cloud storage (70MTCO 2 e), 81% combining axial-only with an 8-year data retention policy (72MTCO 2 e), and 89% combining all three strategies (43MTCO 2 e). Conclusion CT data storage has a large environmental cost, necessitating global action. Various mitigation strategies are achievable in reducing storage-related emissions by up to 89%. Key Points Question Storage of non-essential post-processed CT image series contributes significantly to the accumulating image data storage-associated GHG emissions burden . Findings Modelling predicts emission savings of 69% by avoiding non-essential series storage in staging CTs of UK endometrial cancer patients, with comparable savings globally, based on current practice . Clinical relevance GHG emissions can be substantially reduced by not storing non-essential CT reformats, a mitigation that can be implemented immediately by radiologists. Further GHG mitigation is achievable using cloud storage and data-retention policies . Graphical Abstract

Ultrasound-guided targeted biopsies of CT-based radiomic tumour habitats: technical development and initial experience in metastatic ovarian cancer

Abstract Purpose To develop a precision tissue sampling technique that uses computed tomography (CT)–based radiomic tumour habitats for ultrasound (US)-guided targeted biopsies that can be integrated in the clinical workflow of patients with high-grade serous ovarian cancer (HGSOC). Methods Six patients with suspected HGSOC scheduled for US-guided biopsy before starting neoadjuvant chemotherapy were included in this prospective study from September 2019 to February 2020. The tumour segmentation was performed manually on the pre-biopsy contrast-enhanced CT scan. Spatial radiomic maps were used to identify tumour areas with similar or distinct radiomic patterns, and tumour habitats were identified using the Gaussian mixture modelling. CT images with superimposed habitat maps were co-registered with US images by means of a landmark-based rigid registration method for US-guided targeted biopsies. The dice similarity coefficient (DSC) was used to assess the tumour-specific CT/US fusion accuracy. Results We successfully co-registered CT-based radiomic tumour habitats with US images in all patients. The median time between CT scan and biopsy was 21 days (range 7–30 days). The median DSC for tumour-specific CT/US fusion accuracy was 0.53 (range 0.79 to 0.37). The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76–0.79) while it was lower for the smaller omental metastases (DSC: 0.37–0.53). Conclusion We developed a precision tissue sampling technique that uses radiomic habitats to guide in vivo biopsies using CT/US fusion and that can be seamlessly integrated in the clinical routine for patients with HGSOC. Key Points • We developed a prevision tissue sampling technique that co-registers CT-based radiomics–based tumour habitats with US images. • The CT/US fusion accuracy was high for the larger pelvic tumours (DSC: 0.76–0.79) while it was lower for the smaller omental metastases (DSC: 0.37–0.53).

Early detection of ovarian cancer

Abstract Early detection is the only way to achieve a high cure rate in women with ovarian cancer. Unfortunately, to date, there is no effective strategy for early detection, despite rapidly emerging biomarkers. The low prevalence of ovarian cancer, low specificity and high rates of false positives have been limitations of screening programs. In the hands of experts, transvaginal sonography and MRI are effective tools to characterise ovarian masses. Currently, ongoing efforts in standardization of technique and analysis are likely to improve diagnostic capabilities in clinical routine, as well as the introduction of predictive risk models of malignancy. Radiomics and radiogenomics potentially offer a broad spectrum of complementary information in ovarian cancer diagnosis and treatment. Key Points • Transvaginal sonography and MRI are effective tools to characterise ovarian masses. • Standardisation of imaging technique and implementation of predictive models of risk of malignancy contribute to early detection of ovarian cancer.

Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis

Abstract Objectives To investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC). Methods This retrospective, hypothesis-generating study included 20 patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features which were computed from each tumor site. Three texture features that represented intra- and inter-site tumor heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumor sites and metastasis. Correlations between protein abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation coefficient and the Mann-Whitney U test, whereas the area under the receiver operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. P values &lt; 0.05 were considered significant. Results Four proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p &lt; 0.001, AUC = 0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumor heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p = 0.047, τ = 0.326). Conclusion This study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra- and inter-site heterogeneity, and the abundance of several proteins. Key Points • CT-based texture features of intra- and inter-site tumor heterogeneity correlate with the abundance of several proteins in patients with HGSOC. • CT imaging traits correlate with protein abundance in patients with HGSOC.

Imaging of peritoneal metastases of ovarian and colorectal cancer: joint recommendations of ESGAR, ESUR, PSOGI, and EANM

Abstract Objectives Diagnostic imaging of peritoneal metastases in ovarian and colorectal cancer remains pivotal in selecting the most appropriate treatment and balancing clinical benefit with treatment-related morbidity and mortality. To address the challenges related to diagnostic imaging and detecting and reporting peritoneal metastatic spread, a joint guideline was created by the European Society of Gastrointestinal and Abdominal Radiology (ESGAR), European Society of Urogenital Radiology (ESUR), Peritoneal Surface Oncology Group International (PSOGI), and European Association of Nuclear Medicine (EANM). Methods A targeted literature search was performed and consensus recommendations were proposed using Delphi questionnaires and a five-point Likert scale. Results A total of three Delphi rounds were performed. Consensus was reached on the position of diagnostic imaging for assessment of operability, treatment response monitoring, and follow-up of peritoneal metastases, optimal imaging modality and their technical imaging requirements depending on the indication and how to optimise communication of imaging results by the report and multidisciplinary board discussion. The complete list of recommendations is provided. Conclusion These expert consensus statements aim to guide appropriate indications, acquisition, interpretation, and reporting of imaging for operability assessment, treatment response monitoring, and follow-up of peritoneal metastases in ovarian and colorectal cancer patients. Key Points Question Staging peritoneal metastases (PM) helps to guide clinical decision-making for colorectal and ovarian cancer patients. How can we optimise the use of imaging techniques to assess PM? Findings Imaging plays a crucial role in the detection, operability assessment, treatment response monitoring, and follow-up of peritoneal metastases in colorectal and ovarian cancer patients. Clinical relevance These expert consensus statements aim to guide appropriate indication, acquisition, interpretation, and reporting of imaging for operability assessment, treatment response monitoring, and follow-up of peritoneal metastases in ovarian and colorectal cancer patients.

MRI of pediatric ovarian masses: validation of the O-RADS framework

Abstract Objective The purpose of our study was to test the applicability and implications of using the O-RADS system, which is developed and validated on adults, to review MRI of ovarian masses among pediatric patients. Materials and methods We retrospectively reviewed consecutive MRI examinations from pediatric patients referred to imaging for suspected ovarian lesions, assessing them using the O-RADS framework. Malignancy frequencies among O-RADS classes were reviewed, and we appraised the potential for such approach to split patients into low (O-RADS 1, 2, and 3) and high risk (O-RADS 4 and 5). Multivariate analyses were conducted to review which clinical or imaging variables yielded a significant impact on malignancy, and a simplified reading framework was proposed accordingly. Results 109 female patients were included, with a median age of 13 years (IQR 11–15 years), 7 (7%) presenting with malignant lesions. Malignancy proportions were 0% (95% confidence Interval (CI) 0–35%) for the O-RADS 1 class, 0% (95% CI 0 − 5%) for the O-RADS 2 class, 0% (95% CI 0–14%) for the O-RADS 3 class, 50 (95% CI 1 − 99%) for the O-RADS 4 class, and 75% (95% CI 41–93%) for the O-RADS 5 class. The presence of peritoneal thickening or nodules (p &lt; 0.001), lesion composition (p &lt; 0.001), and absence of intralesional fat (p = 0.051) were individual predictors of malignancy, and the simplified reading framework proposed with such variables identified 11 likely malignant cases, detecting all 7 malignant lesions. Conclusion The O-RADS system may be applied to MRI performed in the pediatric population for ovarian masses, and a simplified reading framework based on O-RADS could also prove useful in such a setting. Key Points Question The Ovarian-Adnexal Reporting and Data System (O-RADS) provides the risk of malignancy of ovarian masses among adults but has not been validated among pediatric patients. Findings Malignancy frequencies for O-RADS classes 1, 2, 3, and 4 were 0, 0, 50%, and 75%, indicating a good accuracy in lesion discrimination. Clinical relevance The Ovarian-Adnexal Reporting and Data System (O-RADS) can be effectively applied to MRI examinations in pediatric patients, enabling accurate classification of findings, with potential for score simplification in this age group.

CT-based radiomic prognostic vector (RPV) predicts survival and stromal histology in high-grade serous ovarian cancer: an external validation study

Abstract Objectives In women with high-grade serous ovarian cancer (HGSOC), a CT-based radiomic prognostic vector (RPV) predicted stromal phenotype and survival after primary surgery. The study's purpose was to fully externally validate RPV and its biological correlate. Materials and methods In this retrospective study, ovarian masses on CT scans of HGSOC patients, who underwent primary cytoreductive surgery in an ESGO-certified Center between 2002 and 2017, were segmented for external RPV score calculation and then correlated with overall survival (OS) and progression-free survival (PFS). A subset of tissue samples subjected to fibronectin immunohistochemistry were evaluated by a gynaeco-pathologist for stromal content. Kaplan–Meier log-rank test and a Cox proportional hazards model were used for outcome analysis. Results Among 340 women with HGSOC, 244 ovarian lesions were available for segmentation in 198 women (mean age 59.8 years, range 34–92). Median OS was 48.69 months (IQR: 27.0–102.5) and PFS was 19.3 months (IQR: 13–32.2). Using multivariate Cox analysis, poor OS was associated with RPV-high (HR 3.17; 95% CI: 1.32–7.60; p = 0.0099), post-operative residual disease (HR 2.04; 95% CI: 1.30–3.20; p = 0.0020), and FIGO stage III/IV (HR 1.79; 95% CI: 1.11–2.86; p = 0.016). Age did not influence OS. RPV-high tissue had higher stromal content based on fibronectin expression (mean 48.9%, SD 10.5%) compared to RPV-low cases (mean 14.9%, SD 10.5%, p &lt; 0.0001). RPV score was not significantly associated with PFS. Conclusion Patients with HGSOC and RPV-high ovarian mass on pre-operative CT had significantly worse OS following primary surgery and a higher stromal content compared to RPV-low masses, externally validating the RPV and its biological interpretation. Key Points Question Can the performance of a previously described RPV in women with HGSOC be replicated when licenced to an external institution? Findings External validation of RPV among 244 ovarian lesions demonstrated that, on multivariate analysis, OS was associated with RPV, stage, and postoperative residual disease, replicating previous findings. Clinical relevance External validation of a radiomic tool is an essential step in translation to clinical applicability and provides the basis for prospective validation. In clinical practice, this RPV may allow more personalized decision-making for women with ovarian cancer being considered for extensive cytoreductive surgery.

Survival impact of the degree of parametrial invasion on MRI in locally advanced cervical cancer

The value of MRI in quantification of parametrial invasion and association with prognosis in locally advanced cervical cancer: the “PLACE” study

This retrospective observational study aims to evaluate the association between the extent of parametrial invasion (PMI) and disease-free survival (DFS) and cancer-specific survival (CSS) in patients with locally advanced cervical cancer (LACC). This study included patients with LACC showing parametrial invasion at Magnetic Resonance Imaging (MRI). They were treated with neoadjuvant chemo-radiotherapy (CT/RT) before undergoing radical hysterectomy. The staging MRIs were reviewed retrospectively. Measurements of maximum PMI (PMI Out of 221 patients, 126 (57%) had non-metastatic lymph nodes (N-), while 95 (43%) had metastatic lymph nodes (N+). The median observation period for all these patients was 73 months (95% confidence interval [CI]: 66-77). The 5-year DFS and CSS probability rates were 75% and 85.7%, respectively, for the N- group and 54.3% and 73.6%, respectively, for the N+ group. A higher PMI The degree of PMI evaluated on MRI affects outcome in N- patients with LACC. The degree of MRI parametrial invasion affects disease-free survival and cancer-specific survival in patients with the International Federation of Gynecology and Obstetrics (FIGO) stage IIB cervical cancer. This MRI finding can be easily incorporated into routine clinical practice. • Visual assessment of parametrial invasion on MRI was not significantly associated with prognosis in locally advanced cervical cancer (LACC). • A greater degree of parametrial invasion is associated with poorer disease-free survival and cancer-specific survival in patients with LACC without metastatic lymph node involvement. • The degree of parametrial invasion at MRI has no correlation with prognosis in LACC with metastatic lymph nodes.

Intratumoral and peritumoral MRI radiomics nomogram for predicting parametrial invasion in patients with early-stage cervical adenocarcinoma and adenosquamous carcinoma

To develop a comprehensive nomogram based on MRI intra- and peritumoral radiomics signatures and independent risk factors for predicting parametrial invasion (PMI) in patients with early-stage cervical adenocarcinoma (AC) and adenosquamous carcinoma (ASC). A total of 460 patients with IB to IIB cervical AC and ASC who underwent preoperative MRI examination and radical trachelectomy/hysterectomy were retrospectively enrolled and divided into primary, internal validation, and external validation cohorts. The original (Ori) and wavelet (Wav)-transform features were extracted from the volumetric region of interest of the tumour (ROI-T) and 3mm- and 5mm-peritumoral rings (ROI-3 and ROI-5), respectively. Then the Ori and Ori-Wav feature-based radiomics signatures from the tumour (RST) and 3 mm- and 5 mm-peritumoral regions (RS3 and RS5) were independently built and their diagnostic performances were compared to select the optimal ones. Finally, the nomogram was developed by integrating optimal intra- and peritumoral signatures and clinical independent risk factors based on multivariable logistic regression analysis. FIGO stage, disruption of the cervical stromal ring on MRI (DCSRMR), parametrial invasion on MRI (PMIMR), and serum CA-125 were identified as independent risk factors. The nomogram constructed by integrating independent risk factors, Ori-Wav feature-based RST, and RS5 yielded AUCs of 0.874 (0.810-0.922), 0.885 (0.834-0.924), and 0.966 (0.887-0.995) for predicting PMI in the primary, internal and external validation cohorts, respectively. Furthermore, the nomogram was superior to radiomics signatures and clinical model for predicting PMI in three cohorts. The nomogram can preoperatively, accurately, and noninvasively predict PMI in patients with early-stage cervical AC and ASC. The nomogram can preoperatively, accurately, and noninvasively predict PMI and facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy in patients with early-stage cervical AC and ASC. The accurate preoperative prediction of PMI in early-stage cervical AC and ASC can facilitate precise treatment decisions regarding chemoradiotherapy or radical hysterectomy. The nomogram integrating independent risk factors, Ori-Wav feature-based RST, and RS5 can preoperatively, accurately, and noninvasively predict PMI in early-stage cervical AC and ASC. The nomogram was superior to radiomics signatures and clinical model for predicting PMI in early-stage cervical AC and ASC.

Non-contrast MRI can accurately characterize adnexal masses: a retrospective study

Abstract Objective To determine the accuracy of interpretation of a non-contrast MRI protocol in characterizing adnexal masses. Methods and materials Two hundred ninety-one patients (350 adnexal masses) who underwent gynecological MRI at our institution between the 1st of January 2008 and the 31st of December 2018 were reviewed. A random subset (102 patients with 121 masses) was chosen to evaluate the reproducibility and repeatability of readers’ assessments. Readers evaluated non-contrast MRI scans retrospectively, assigned a 5-point score for the risk of malignancy and gave a specific diagnosis. The reference standard for the diagnosis was histopathology or at least one-year imaging follow-up. Diagnostic accuracy of the non-contrast MRI score was calculated. Inter- and intra-reader agreement was analyzed with Cohen’s kappa statistics. Results There were 53/350 (15.1%) malignant lesions in the whole cohort and 20/121 (16.5%) malignant lesions in the random subset. Good agreement between readers was found for the non-contrast MRI score (к = 0.73, 95% confidence interval [CI] 0.58–0.86) whilst the intra-reader agreement was excellent (к = 0.81, 95% CI 0.70–0.88). The non-contrast MRI score value of ≥ 4 was associated with malignancy with a sensitivity of 84.9%, a specificity of 95.9%, an accuracy of 94.2% and a positive likelihood ratio of 21 (area under the receiver operating curve 0.93, 95% CI 0.90–0.96). Conclusion Adnexal mass characterization on MRI without the administration of contrast medium has a high accuracy and excellent inter- and intra-reader agreement. Our results suggest that non-contrast studies may offer a reasonable diagnostic alternative when the administration of intravenous contrast medium is not possible. Key Points • A non-contrast pelvic MRI protocol may allow the characterization of adnexal masses with high accuracy. • The non-contrast MRI score may be used in clinical practice for differentiating benign from malignant adnexal lesions when the lack of intravenous contrast medium precludes analysis with the O–RADS MRI score.

Evaluation of patients with advanced epithelial ovarian cancer before primary treatment: correlation between tumour burden assessed by [18F]FDG PET/CT volumetric parameters and tumour markers HE4 and CA125

Accurate assessment of disease extent is required to select the best primary treatment for advanced epithelial ovarian cancer patients. Estimation of tumour burden is challenging and it is usually performed by means of a surgical procedure. Imaging techniques and tumour markers can help to estimate tumour burden non-invasively. 2-[ We included 66 patients who underwent 2-[ wb_MTV and wb_TLG were found to be significantly correlated with serum CA125 and HE4 concentrations. The strongest correlation was observed between HE4 and wb_MTV40 (r = 0.62, p < 0.001). Pearson's correlation coefficients between peritoneal carcinomatosis MTV40 and tumour markers were 0.61 (p < 0.0001) and 0.29 (p = 0.02) for HE4 and CA125 respectively. None of these tumour markers showed a positive correlation with tumour load outside the abdominal cavity assessed by volumetric parameters. HE4 performs better than CA125 to predict metabolic tumour burden in high-grade epithelial ovarian cancer before primary treatment. 2-[18F]FDG PET/CT volumetric parameters arise as feasible tools for the objective assessment of tumour load and its anatomical distribution. These results support the usefulness of HE4 and PET/CT to improve the stratification of these patients in clinical practice. • In patients with high-grade advanced ovarian epithelial carcinoma, both CA125 and HE4 correlate to whole-body tumour burden assessed by PET/CT before primary treatment. • HE4 estimates peritoneal disease much better than CA125. • PET/CT volumetric parameters arise as feasible tools for the objective assessment of tumour load and its anatomical distribution.

Comparison of O-RADS, GI-RADS, and IOTA simple rules regarding malignancy rate, validity, and reliability for diagnosis of adnexal masses

The American College of Radiology (ACR) recently published the ovarian-adnexal reporting and data system (O-RADS) to provide guidelines to physicians who interpret ultrasound (US) examinations of adnexal masses (AM). This study aimed to compare the O-RADS with two other well-established US classification systems for diagnosis of AM. This retrospective multicenter study between May 2016 and December 2019 assessed consecutive women with AM detected by the US. Five experienced consultant radiologists independently categorized each AM according to O-RADS, gynecologic imaging reporting and data system (GI-RADS), and international ovarian tumor analysis (IOTA) simple rules. Pathology and adequate follow-up were used as reference standards for calculating the validity of three US classification systems for diagnosis of AM. Kappa statistics were used to assess the inter-reviewer agreement (IRA). A total of 609 women (mean age, 48 ± 13.7 years; range, 18-72 years) with 647 AM were included. Of the 647 AM, 178 were malignant and 469 were benign. Malignancy rates were comparable to recommended rates by previous literature in O-RADS and IOTA, but higher in GI-RADS. O-RADS had significantly higher sensitivity for malignancy than GI-RAD and IOTA (p = 0.003 and 0.0007, respectively), but non-significant slightly lower specificity (p > 0.05). O-RADS, GI-RADS, and IOTA showed similar overall IRA (κ = 0.77, 0.69, and 0.63, respectively) with a tendency toward higher IRA with O-RADS than with GI-RADS and IOTA. O-RADS compares favorably with GI-RADS and IOTA. O-RADS had higher sensitivity than GI-RADS and IOTA simple rules with relatively similar specificity and reliability. • The malignancy rates were comparable to recommended rates by previous literature in O-RADS and IOTA, but higher in GI-RADS. • The O-RADS had significantly higher sensitivity for malignancy than GI-RADS and IOTA (96.8% vs 92.7% and 92.1%; p = 0.003 and 0.0007, respectively), but non-significant slightly lower specificity (92.8% vs 93.6% and 93.2%, respectively; p > 0.05). • The O-RADS, GI-RADS, and IOTA showed similar overall inter-reviewer agreement (IRA) (κ = 0.77, 0.69, and 0.63, respectively), with a tendency toward higher IRA with O-RADS than with GI-RADS and IOTA.

Microcystic pattern and shadowing are independent predictors of ovarian borderline tumors and cystadenofibromas in ultrasound

To determine the sonographic characteristics of borderline tumors (BoTs) and cystadenofibromas (CAFs). Preoperative sonograms from consecutive patients who had at least one primary epithelial tumor in the adnexa were retrospectively collected. All tumors were described using the International Ovarian Tumor Analysis terminology. Ultrasound variables were tested using multinomial logistic regression after univariate analysis. A total of 650 patients were included in this study. Of these, 110 had a CAF, 128 had a BoT, 249 had a cystadenoma (CAD), and 163 had a cystadenocarcinoma (CAC). Nearly half of CAFs and more than half of BoTs and CACs appeared to be unilocular and multilocular solid on the ultrasound images, while CADs were predominantly uni- or multilocular (p < 0.001). Overall, shadowing was identified in 82/650 cases. Sixty-five of 110 (59.1%) CAFs exhibited an acoustic shadow, compared with only 4/249 (1.6%) in CADs, 7/128 (5.5%) in BoTs, and 6/163 (3.7%) in CACs (p < 0.001). Furthermore, 112/650 cases demonstrated microcystic pattern (MCP). Sixty-eight of 128 (53.1%) BoTs exhibited MCP, compared with only 5/249 (2.0%) in CADs, 19/163 (11.7%) in CACs, and 20/110 (18.2%) in CAFs (p < 0.001). Logistic regression analysis revealed that shadowing is an independent predictor of CAFs, while MCP is an independent predictor of BoTs. Sonographic findings for CAFs and BoTs were complex and partly overlapped with those for CACs. However, proper recognition and utilization of shadowing or MCP may help to correctly discriminate CAFs and BoTs. • Sonographic findings for borderline tumors and cystadenofibromas are complex and mimic malignancy. • Microcystic pattern and shadowing are independent predictors of borderline tumors and cystadenofibromas respectively.

Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms

To evaluate the efficiency of 2- and 3-class classification predictive tasks constructed from radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic (PK) protocol in discriminating among benign, borderline, and malignant ovarian tumors. One hundred and four ovarian lesions were evaluated using preoperative DCE-MRI. Radiomics features were extracted from 7 types of DCE-MR images. To explore the differential ability of radiomics between three types of ovarian tumors, two- and three-class classification tasks were established. The 2-class classification task was divided into three subtasks: benign vs. borderline (task A), benign vs. malignant (task B), and borderline vs. malignant (task C). For the 3-class classification task, 104 lesions were randomly divided into training (72 lesions) and validation (32 lesions) cohorts. The discrimination abilities of the radiomics signatures were established with the training cohort and tested with the independent validation cohort. The predictive performance of the task was evaluated by receiver operating characteristic (ROC) curve, calibration curve analysis, and decision curve analysis (DCA). For the 2-class classification task, the combination of PK radiomics signatures model (PK model) showed a good diagnostic ability with the highest area under the ROC curves (AUCs) of 0.899, 0.865, and 0.893 for tasks A, B, and C, respectively. Additionally, the 3-class classification task demonstrated a good discrimination performance with AUCs of 0.893, 0.944, and 0.891 for the benign, borderline, and malignant groups, respectively. Radiomics analysis based on the DCE-MRI PK protocol showed promise for discriminating among benign, borderline, and malignant ovarian tumors. • Two-class classification predictive task of DCE-MRI PK protocol enabled the classification of 3 categories of ovarian tumors through the pairwise comparison strategy with a perfect diagnostic ability. • Three-class classification predictive task maintained good performance to effectively judge each category of ovarian tumors directly.

MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers

Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC. In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.

MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes

To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC). In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC. Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types. The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease. • The combined radiomics model resulted in a better predictive model than that from a single sequence model. • The traditional model showed higher classification accuracy than the combined radiomics model. • Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

Application of the amide proton transfer-weighted imaging and diffusion kurtosis imaging in the study of cervical cancer

To analyze the value of amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) in differentiating cervical cancer (CC) pathological type, grade, and stage. One hundred and twelve women underwent pelvic APTWI and DKI. The magnetization transfer ratio asymmetry (MTRasym, 3.5 ppm), apparent kurtosis coefficient (K The MTRasym(3.5 ppm) and D Both DKI and APTWI can be used in preliminary evaluations of CC, but DKI has advantages in the identification of pathological type, grade, and stage. • PTWI and DKI provide new information regarding cervical cancer. • MTRasym(3.5 ppm), D

Preoperative prediction of parametrial invasion in early-stage cervical cancer with MRI-based radiomics nomogram

To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC). All 137 patients with ECC (FIGO stages IB-IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient's age, and pathological grade; its discrimination and calibration performances were estimated. For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682-0.911) vs 0.946 (95% CI, 0.899-0.994), and 0.780 (95% CI, 0.641-0.920) vs 0.921 (95% CI, 0.832-1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient's age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933-1) and 0.941 (95% CI, 0.868-1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement. The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC. • No previously reported study that has utilized radiomics nomogram to preoperatively predict PMI for patients with ECC. • Radiomics model involves radiomics features extracted from joint T2WI and DWI which characterize the heterogeneity between tumors in patients with ECC. • Radiomics nomogram can assist clinicians with individualized treatment decision-making for patients with ECC.

Diagnostic performance of MR imaging in evaluating prognostic factors in patients with cervical cancer: a meta-analysis

This study aims to determine the diagnostic performance of conventional magnetic resonance imaging (MRI) in assessing the distance between the tumor and the internal os, stromal infiltration, lymph node metastasis, and parametrial invasion in patients with cervical cancer. A systematic English-language literature search of conventional MRI in the evaluation of human cervical cancer was performed in the PubMed, Cochrane Library, Embase, and Web of Science databases from 1995 to 2018. The pooled sensitivity, specificity, diagnostic odds ratio (DOR), and positive and negative likelihood ratios (PLR and NLR) of all studies were calculated. The results were then plotted in a hierarchical summary receiver operating characteristic (HSROC) plot, and meta-regression and subgroup analyses of the parametrial invasion were also performed. The pooled sensitivity, specificity, DOR, PLR, and NLR were 86%, 97%, 167.91, 24.74, and 0.15, respectively, in evaluating the internal os involvement (6 studies, 454 patients); 87%, 91%, 73.41, 10.22, and 0.14, respectively, in evaluating the stromal infiltration (11 studies, 672 patients); 51%, 89%, 8.63, 4.72, and 0.55, respectively, in evaluating the lymph node metastasis (15 studies, 997 patients); and 75%, 92%, 34.01, 9.38, and 0.28, respectively, in evaluating the parametrial invasion (19 studies, 1748 patients). The meta-regression of the parametrial invasion showed that the application of contrast enhancement was a significant factor affected the heterogeneity (p = 0.039). Conventional MRI can accurately evaluate the distance between the tumor and the internal os, as well as stromal infiltration, and performs well in diagnosing the parametrial invasion. However, this method exhibited a limited ability in diagnosing the lymph node metastasis. • MRI can help clinicians to accurately assess the distance between the tumor and the internal os, stromal infiltration, and parametrial invasion in patients with uterine cervical neoplasms. • MRI exhibits a limited ability in diagnosing the lymph node metastasis. • Management of patients with uterine cervical neoplasms becomes more appropriate.

Strain elastography as an early predictor of long-term prognosis in patients with locally advanced cervical cancers treated with concurrent chemoradiotherapy

To explore the value of strain elastography as an early predictor of long-term prognosis in patients with locally advanced cervical cancers treated with concurrent chemoradiotherapy (CCRT). Strain elastography examinations were performed on 45 patients with locally advanced cervical cancers at 3 time points: prior to CCRT, and at 1 and 2 weeks after the start of CCRT. The maximum tumor diameter (D After a median follow-up of 30 months (range, 12-36 months), 36 of 45 (80%) patients were disease free. The D Strain elastography may be useful as an early predictor of long-term outcomes after CCRT for patients with cervical cancer. • The D

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study

To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Evaluation of amide proton transfer-weighted imaging for endometrial carcinoma histological features: a comparative study with diffusion kurtosis imaging

To investigate whether amide proton transfer-weighted imaging (APTWI) and diffusion kurtosis imaging (DKI) can be used to evaluate endometrial carcinoma (EC) in terms of clinical type, histological grade, subtype, and Ki-67 index. Eighty-eight patients with EC underwent pelvic DKI and APTWI. The non-Gaussian diffusion coefficient (D The MTRasym (3.5 ppm) and K Both DKI- and APTWI-related parameters have potential as imaging markers in estimating the histological features of EC, while DKI shows better performance than APTWI in this study. • DKI and APTWI can be used to preliminarily evaluate the histological characteristics of endometrial carcinoma (EC). • The K