Acta Radiologica

Validation of models in predicting residual disease in ovarian cancer: comparing CT urography with PET/CT

Background Many ovarian cancer (OC) residual-disease prediction models were not externally validated after being constructed, the clinical applicability needs to be evaluated. Purpose To compare computed tomography urography (CTU) with PET/CT in validating models for predicting residual disease in OC. Material and Methods A total of 250 patients were included during 2018–2021. The CTU and PET/CT scans were analyzed, generating CT-Suidan, PET-Suidan, CT-Peking Union Medical College Hospital (PUMC), and PET-PUMC models. All imagings were evaluated by two readers independently, then compared to pathology. According to surgical outcomes, all patients were divided into the R0 group, with no visible residual disease, and the R1 group, with any visible residual disease. Logistic regression was used to assess the discrimination and calibration abilities of each model. Results CTU and PET/CT showed good diagnostic performance in predicting OC peritoneal metastases based on the Suidan and PUMC model (all the accuracies >0.8). As for model evaluation, the value of correct classification of the CT-Suidan, PET-Suidan, CT-PUMC, and PET-PUMC models was 0.89, 0.84, 0.88, and 0.83, respectively, representing stable calibration. The areas under the curve (AUC) of these models were 0.95, 0.90, 0.91, and 0.90, respectively. Furthermore, the accuracy of these models at the optimal threshold value (score 3) was 0.75, 0.78, 0.80, and 0.80, respectively. All two-paired comparisons of the AUCs and accuracies did not show a significant difference (all P > 0.05). Conclusion CT-Suidan, CT-PUMC, PET-Suidan, and PET-PUMC models had equal abilities in predicting the residual disease of OC. The CT-PUMC model was recommended for its economic and user-friendly characteristics.

MRI-based radiomics analysis to evaluate the clinicopathological characteristics of cervical carcinoma: a multicenter study

Background Preoperative prediction of clinical pathological indicators of cervical cancer (CC) is of great significance to the formulation of personalized treatment plans for CC. Purpose To investigate magnetic resonance imaging (MRI) radiomics analysis for the evaluation of pathological types, tumor grade, FIGO stage, and lymph node metastasis (LNM) of CC. Material and Methods A total of 235 patients with CC from three institutes were enrolled in the study. All patients underwent T2/SPAIR and contrast-enhanced T1-weighted (CE-T1WI) imaging scans before radical hysterectomy by pelvic lymph node dissection surgery. Radiomics features extracted from T2/SPAIR and CE-T1WI imaging were selected by the least absolute shrinkage and selection operator (LASSO) methods for further radiomics signature calculation. These radiomic features were used to construct regression and decision tree models to evaluate the performance of radiomic features in distinguishing clinicopathological indicators. Results The area under the curve (AUC) of T2/SPAIR and CE-T1WI imaging were 0.777 and 0.750, respectively, for differentiating between adenocarcinoma and squamous cell carcinoma. From the two sequences, the AUC of the verification group that distinguished low FIGO stage from high FIGO stage was 0.716 and 0.676, respectively. The AUC for moderately well and poorly differentiated tumors were 0.729 on T2/SPAIR and 0.749 on CE-T1WI imaging. The AUC of the verification groups for LNM was 0.730 and 0.618 on T2/SPAIR and CE-T1WI imaging, respectively. Conclusion MRI radiomics features can be used as a non-invasive method to evaluate the clinicopathological indexes of CC and provide an important auxiliary examination method for patients to determine individualized treatment plans before operation.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI

Background Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers. Purpose To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC. Material and Methods We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann–Whitney U test, Pearson’s Chi-squared test, or Fisher’s exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis. Results All histogram parameters of Ktrans, kep, and ve were not significantly different between EOC patients with and without recurrence ( P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of Ktrans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and kep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of Ktrans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively. Conclusion Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

Clinical value of O-RADS combined with serum CA125 and HE4 for the diagnosis of ovarian tumours

Background Ovarian tumors (OTs) are common gynecological tumors in women. It is very important to correctly distinguish benign and malignant OTs. Purpose To assess the diagnostic performance of the American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) and evaluate the clinical value of O-RADS combined with serum carbohydrate antigen 125 (CA125) and human epididymis protein 4 (HE4) in differentiating benign from malignant OTs. Material and Methods A retrospective analysis was performed on 431 cases including pathology and clinical data. The receiver operating characteristic (ROC) curve was drawn, and sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated. Results In premenopausal women, O-RADS and O-RADS combined with serum CA125 and HE4 showed sensitivity at 92.2% and 94.8%, specificity at 91.8% and 93.4%, and accuracy at 91.9% and 93.8%, respectively. In postmenopausal women, the sensitivity of O-RADS, O-RADS combined with serum CA125 and HE4 was 94.8% and 95.8%, specificity was 83.9% and 93.6%, and accuracy was 90.5% and 95.6%, respectively. The sensitivity, specificity, and accuracy of O-RADS combined with CA125 and HE4 in premenopausal and postmenopausal women were higher than that of O-RADS ( P<0.05). Conclusion O-RADS has high diagnostic performance in OTs. When O-RADS is combined with CA125 and HE4 in the diagnosis of OTs, the sensitivity and specificity are improved, which is helpful to improve the diagnostic efficiency of OTs and has high clinical application value.

Pre-treatment prediction of early response to chemoradiotherapy by quantitative analysis of baseline staging FDG-PET/CT and MRI in locally advanced cervical cancer

Background Early prediction of response to concurrent chemoradiotherapy (cCRT) could aid to further optimize treatment regimens for locally advanced cervical cancer (LACC) in the future. Purpose To explore whether quantitative parameters from baseline (pre-therapy) magnetic resonance imaging (MRI) and FDG-PET/computed tomography (CT) have potential as predictors of early response to cCRT. Material and Methods Forty-six patients with LACC undergoing cCRT after staging with FDG-PET/CT and MRI were retrospectively analyzed. Primary tumor volumes were delineated on FDG-PET/CT, T2-weighted (T2W)-MRI and diffusion-weighted MRI (DWI) to extract the following quantitative parameters: T2W volume; T2W signalmean; DWI volume; ADCmean; ADCSD; MTV42%; and SUVmax. Outcome was the early treatment response, defined as the residual tumor volume on MRI 3–4 weeks after start of external beam radiotherapy with chemotherapy (before the start of brachytherapy): patients with a residual tumor volume <10 cm3 were classified as early responders. Imaging parameters were analyzed together with FIGO stage to assess their performance to predict early response, using multivariable logistic regression analysis with bi-directional variable selection. Leave-one-out cross-validation with bootstrapping was used to simulate performance in a new, independent dataset. Results T2W volume (OR 0.94, P = 0.003) and SUVmax (OR 1.15, P = 0.18) were identified as independent predictors in multivariable analysis, rendering a model with an AUC of 0.82 in the original dataset, and AUC of 0.68 (95% CI 0.41–0.81) from cross-validation. Conclusion Although the predictive performance achieved in this small exploratory dataset was limited, these preliminary data suggest that parameters from baseline MRI and FDG-PET/CT (in particular pre-therapy tumor volume) may contribute to prediction of early response to cCRT in cervical cancer.

Reproducibility of radiomics features derived from intravoxel incoherent motion diffusion-weighted MRI of cervical cancer

Background The reproducibility of intravoxel incoherent motion (IVIM)-based radiomics studies in humans has not been reported. Purpose To determine the inter- and intra-observer variability on the reproducibility of IVIM-based radiomics features in cervical cancer (CC). Material and Methods The IVIM images of 25 patients with CC were retrospectively collected. Based on the high-resolution T2-weighted images, the regions of interest (ROIs) were independently delineated twice in diffusion-weighted images at a b value of 1000 s/mm2 (interval time was one month) by two radiologists. This was done at the largest transversal cross-sections of the tumors. The ROI was subsequently used in apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps derived from IVIM images. In total, 105 radiomics features were then finally extracted from the IVIM-derived maps. The inter- and intra-observer reproducibility of IVIM-derived features was then evaluated using the intraclass correlation coefficient. Results Inter- and intra-observer variability affected the reproducibility of radiomics features. D* map had 100% and 95% reproducible features, ADC map had 89% and 93%, D map had 97% and 86%, while f map had 54% and 62% reproducible features with good to excellent reliability in the intra-observer analysis. Similarly, D* map had 90% and 94%, ADC map had 85% and 70%, D map had 81% and 78%, while f map had 41% and 93% reproducible features with good to excellent reliability in the inter-observer analysis. Conclusion Inter- and intra-observer variability can affect radiomics analysis. Cognizant to this, multicenter studies should pay more attention to intra- and inter-observer variability.

Volume computed tomography perfusion as a predictive marker for treatment response to concurrent chemoradiotherapy in cervical cancer: a prospective study

Background Computed tomography perfusion (CTP) can provide information on blood perfusion as a reliable marker of tumor response to therapy. Purpose To assess the role of volume CTP (vCTP) parameters in predicting treatment response to concurrent chemoradiotherapy (CCRT) for cervical cancer. Material and Methods Thirty-three patients with cervical cancer underwent vCTP. Three CTP parameters of cervical cancer—including arterial flow (AF), blood volume (BV), and permeability surface (PS)—were measured in two different ways: the region of interest incorporating the “local hot” with the highest enhancement and “cold spot” with the lowest enhancement; and “whole-tumor” measurements. The patients were divided into non-residual and residual tumor groups according to the short-term response to treatment. The clinical and perfusion parameters were compared between the two groups. Results There was no significant difference in age, body mass index, FIGO stage, pathological grade, or pretreatment tumor size between the two groups ( P > 0.05). The non-residual tumor group had higher pretreatment AF in high-perfusion and low-perfusion subregions than the residual tumor group ( P <0.05), but the AF in whole-tumor regions was not different between the two groups ( P > 0.05). There were no differences in BV and PS between the two groups ( P > 0.05). The diagnostic potency of AF in the low-perfusion subregion was higher than that in the high-perfusion subregion. Conclusion vCTP parameters are valuable for the prediction of short-term effects. The AF in the low-perfusion subregion was a more effective index for predicting treatment response to CCRT of cervical cancer.

Influence of menstrual status and pathological type on the apparent diffusion coefficient in cervical cancer: a primary study

Background Apparent diffusion coefficient (ADC) value is an important quantitative parameter in the research of cervical cancer, affected by some factors. Purpose To investigate the effect of pathological type and menstrual status on the ADC value of cervical cancer. Material and Methods A total of 352 individuals with pathologically confirmed cervical cancer between January 2015 to December 2017 were retrospectively enrolled in this study, including 317 cases with squamous cell carcinomas (SCC) and 35 cases with adenocarcinomas (AC); 177 patients were non-menopausal and 175 were menopausal. All patients underwent a routine 3.0-T magnetic resonance imaging (MRI) scan and diffusion-weighted imaging (DWI) examination using b-values of 0, 800, and 1000 s/mm2. Three parameters including mean ADC (ADCmean), maximum ADC (ADCmax), and minimum ADC (ADCmin) of cervical cancer lesions were measured and retrospectively analyzed. Independent samples t-test was used to compare the difference of ADC values in different menstrual status and pathological types. Results In all menopausal and non-menopausal patients, the ADCmean and ADCmin values of SCC were lower than those of AC ( P<0.05), the ADCmax of two pathological types showed no statistical difference ( P > 0.05). In menopausal patients, the ADCmean, ADCmax, and ADCmin values of SCC were not statistically different compared with those of AC ( P > 0.05). The ADCmean, ADCmax, and ADCmin values of different pathological types cervical cancers in non-menopausal patients were all higher than those in menopausal patients ( P<0.05). Conclusion The ADC values of the cervical cancers were different in different pathological types and were also affected by menstrual status.

Multiparametric MRI-based radiomics analysis: differentiation of subtypes of cervical cancer in the early stage

Background There are significant differences in outcomes for different histological subtypes of cervical cancer (CC). Yet, it is difficult to distinguish CC subtypes using non-invasive methods. Purpose To investigate whether multiparametric magnetic resonance imaging (MRI)-based radiomics analysis can differentiate CC subtypes and explore tumor heterogeneity. Material and Methods This study retrospectively analyzed 96 patients with CC (squamous cell carcinoma [SCC] = 50, adenocarcinoma [AC] = 46) who underwent pelvic MRI before surgery. Radiomics features were extracted from the tumor volumes on five sequences (sagittal T2-weighted imaging [T2SAG], transverse T2-weighted imaging [T2TRA], sagittal contrast-enhanced T1-weighted imaging [CESAG], transverse contrast-enhanced T1-weighted imaging [CETRA], and apparent diffusion coefficient [ADC]). Clustering and logistic regression were used to examine the distinguishing capabilities of radiomics features extracted from five different MR sequences. Results Among the 105 extracted radiomics features, there were 51, 38, 37, and 2 features that showed intergroup differences for T2SAG, T2TRA, ADC, and CESAG, respectively (all P < 0.05). AC had greater textural heterogeneity than SCC ( P < 0.05). Upon unsupervised clustering of significantly different features, T2SAG achieved the highest accuracy (0.844; sensitivity = 0.920; specificity = 0.761). The largest area under the curve (AUC) for classification ability was 0.86 for T2SAG. Hence, the radiomics model from five combined MR sequences (AUC = 0.89; accuracy = 0.81; sensitivity = 0.67; specificity = 0.94) exhibited better differentiation ability than any MR sequence alone. Conclusion Multiparametric MRI-based radiomics models may be a promising method to differentiate AC and SCC. AC showed more heterogeneous features than SCC.

Perfusion parameters of intravoxel incoherent motion based on tumor edge region of interest in cervical cancer: evaluation of differentiation and correlation with dynamic contrast-enhanced MRI

Background Intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) is a functional magnetic resonance imaging (MRI) sequence. Purpose To evaluate the value of perfusion parameters derived from IVIM-DWI based on tumor edge region of interest (ROI) in differentiation in cervical cancer and investigate the relationship between IVIM and dynamic contrast-enhanced MRI (DCE-MRI). Material and Methods Thirty-three patients with pathologically diagnosed squamous cell carcinoma who underwent IVIM-DWI (nine b-values: 1–1000 s/mm2) and DCE-MRI were retrospectively assessed in this study. Parameters of IVIM (D, f, D*, fD*) and quantitative parameters of DCE-MRI (Ktrans, Kep, Ve) were derived using tumor edge ROI. Mann–Whitney U test was used to compare parameters between pathological grades and receiver operating characteristic (ROC) curves were used. Pearson’s correlation coefficient (r) evaluated the correlation between perfusion parameters derived from IVIM and DCE-MRI. Results The poorly differentiated group showed the significantly lower D value and the higher f, Ktrans and Kep values than the well-to-moderately differentiated group ( P < 0.05). ROC curves indicated that f < 26%, Ktrans <0.38/min, and Kep <1.62/min could differentiate the poorly differentiated group from the well-to-moderately differentiated group (AUC 0.753–0.808). Significantly positive correlations were found between f and Ktrans (r = 0.422, P = 0.014) and between fD* and Ktrans (r = 0.448, P = 0.009). Conclusion Perfusion parameters derived from IVIM based on tumor edge ROI may offer additional value in differentiation in cervical cancer, and the IVIM perfusion parameters showed moderate positive correlations with quantitative perfusion parameters from DCE-MRI, while f and fD* showed promising significance.

Application of apparent diffusion coefficient values derived from diffusion-weighted imaging for assessing different sized metastatic lymph nodes in cervical cancers

Background Lymph nodes metastasis is an important factor affecting survival rate and recurrence in cervical cancer patients. Currently, diagnosis of metastatic lymph nodes is mainly based on morphological changes on imaging. However, it is difficult to differentiate normal-sized metastatic lymph nodes with short axis of 5-10mm. Purpose To assess the diagnostic value of apparent diffusion coefficient (ADC) for discriminating different-sized metastatic lymph nodes in patients with cervical cancers. Material and Methods Pathologically confirmed cervical cancer patients were documented from January 2013 to July 2018 in our hospital. A total of 133 patients who underwent conventional MRI and diffusion-weighted imaging with complete pathology were finally enrolled. A total of 157 lymph nodes were harvested and analyzed. All lymph nodes were divided into three groups according to pathology and their short axis (S) measured on axial T2-weighted imaging: normal-sized (5 mm<S<10 mm) benign lymph nodes (Group 1); normal-sized (5 mm<S<10 mm) metastatic lymph nodes (Group 2); enlarged (S≥10 mm) metastatic lymph nodes (Group 3). Mean ADC (ADCmean), minimum ADC (ADCmin), and maximum ADC (ADCmax) values of lymph nodes were analyzed and compared among the three groups. Results ADCmean of Groups 1 and 2 were significantly larger than those of Group 3 ( P<0.001, P=0.005, respectively). ADCmin of Group 1 were significantly larger than those of Groups 2 and 3 ( P<0.001, P<0.001, respectively). ADCmax was not statistically different among the three groups. ADCmean had the relatively highest area under the curve (AUC) of 0.644 for assessing enlarged metastatic lymph nodes, with a sensitivity of 64.4% and specificity of 67.9%. ADCmin had the highest AUC of 0.758 for assessing normal-sized metastatic lymph nodes, with a sensitivity of 84.7% and specificity of 60.7%. Conclusion Diffusion-weighted imaging can be used to differentiate enlarged metastatic lymph nodes from benign lymph nodes, and ADCmin can be further used to identify micro-metastasis in normal-sized lymph nodes.

Impact of different b-value combinations on radiomics features of apparent diffusion coefficient in cervical cancer

Background The impact of variable b-value combinations on apparent diffusion coefficient (ADC)-based radiomics features has not been fully addressed in literature. Purpose To investigate the correlation between radiomics features extracted from ADC maps and various b-value combinations in cervical cancer. Material and Methods Diffusion-weighted images (b-values: 0, 600, 800, and 1000 s/mm2) of 20 patients with cervical cancer were included. Tumors were identified with the largest transversal cross-section and manually segmented by radiologist. For each b-value combination, 92 radiomics features were extracted and coefficient of variance (CV) was used to evaluate the robustness of radiomics features with different b-value combinations. Features with CV > 5% were normalized by the mean feature variation across the group. Results Out of a total of 92 radiomics features, 18 were classified as robust features with CV ≤5%. Among the rest (CV > 5%), 11, 23, and 40 features demonstrated 5%< CV ≤10%, 10%< CV ≤20%, and CV > 20%, respectively. A subset of features in each category (CV > 5%) showed strong correlation with the b-value combination variation, including 44% (7/16) features in gray level co-occurrence matrix, 62% (8/13) features in gray level dependence matrix, 64% (9/14) features in first order, 50% (8/16) features in gray level run length matrix, 57% (8/14) features in gray level size matrix, and 20% (1/5) features in neighborhood gray-tone difference matrix. Conclusions Variations in b-value combinations demonstrated impact on radiomics features extracted from ADC maps for cervical cancer. The radiomics features with CV <5% can be considered as robust features and are recommended to be used in multicenter radiomics studies.

Reduced field-of-view diffusion-weighted MR imaging for assessing the local extent of uterine cervical cancer

Background Recently, the evaluation of the tumor size and local extension of early-stage uterine cervical cancer on magnetic resonance imaging is important for the accurate clinical staging and to determine the indication of less extensive surgery such as fertility sparing radical trachelectomy. Purpose To compare the diagnostic ability of reduced field-of-view diffusion-weighted imaging with those of three-dimensional (3D) contrast-enhanced T1-weighted imaging and T2-weighted imaging for assessing the tumor margin delineation and local extent of uterine cervical cancer. Material and Methods 3T magnetic resonance images, including T2-weighted imaging, reduced field-of-view diffusion-weighted imaging, and 3D contrast-enhanced T1-weighted imaging, in 27 women with surgically proven cervical cancer (19 FIGO stage IB1, 3 IB2, and 5 IIA1) were retrospectively evaluated. Tumor margins and local tumor extent, including the presence of invasion to parametrium and vagina were evaluated on both sagittal and oblique axial (short axis) images; the results were compared with histologically confirmed tumor extension. Results Reduced field-of-view diffusion-weighted imaging diagnosed the tumor margins, which was more accurate than T2-weighted imaging ( P<0.001) and slightly better than 3D contrast-enhanced T1-weighted imaging. Reduced field-of-view diffusion-weighted imaging could define the tumor margins well even in small lesions (≤ 20 mm). Histological examination revealed parametrial invasion in two cases (clinically under-staged) and vaginal invasion in four cases. Reduced field-of-view diffusion-weighted imaging could demonstrate local extension of all lesions, which was more accurate than clinical examination and T2-weighted imaging. Conclusion Addition of reduced field-of-view diffusion-weighted imaging may improve the staging accuracy of magnetic resonance imaging for cervical cancer in assessing the local tumor extent.

Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer

Background Deep learning (DL) has been used on medical images to grade, differentiate, and predict prognosis in many tumors. Purpose To explore the effect of computed tomography (CT)-based deep learning nomogram (DLN) for predicting cervical cancer lymph node metastasis (LNM) before surgery. Material and Methods In total, 418 patients with stage IB-IIB cervical cancer were retrospectively enrolled for model exploration (n = 296) and internal validation (n = 122); 62 patients from another independent institution were enrolled for external validation. A convolutional neural network (CNN) was used for DL features extracting from all lesions. The least absolute shrinkage and selection operator (Lasso) logistic regression was used to develop a deep learning signature (DLS). A DLN incorporating the DLS and clinical risk factors was proposed to predict LNM individually. The performance of the DLN was evaluated on internal and external validation cohorts. Results Stage, CT-reported pelvic lymph node status, and DLS were found to be independent predictors and could be used to construct the DLN. The combination showed a better performance than the clinical model and DLS. The proposed DLN had an area under the curve (AUC) of 0.925 in the training cohort, 0.771 in the internal validation cohort, and 0.790 in the external validation cohort. Decision curve analysis and stratification analysis suggested that the DLN has potential ability to generate a personalized probability of LNM in cervical cancer. Conclusion The proposed CT-based DLN could be used as a personalized non-invasive tool for preoperative prediction of LNM in cervical cancer, which could facilitate the choice of clinical treatment methods.

Whole-tumor histogram analysis of apparent diffusion coefficient for differentiating adenosquamous carcinoma and adenocarcinoma from squamous cell carcinoma in patients with cervical cancer

Background Differentiating adenosquamous carcinoma (ASC) and adenocarcinoma (AC) from squamous cell carcinoma (SCC) precisely is crucial for treatment strategy and prognosis prediction in patients with cervical cancer (CC). Purpose To differentiate ASC and AC from SCC in patients with CC using the apparent diffusion coefficient (ADC) histogram analysis. Material and Methods A total of 118 patients with histologically diagnosed ASC, AC, and SCC were included. The ADC histogram parameters were extracted from ADC maps. Receiver operating characteristic analysis was performed to evaluate the diagnostic performance of each ADC histogram parameter in differentiating the subtypes of CC. The predictors for histologic subtypes were further selected using univariate and multivariate logistic regression analyses. Results The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of the ASC were significantly lower than those of the AC; and ADCkurtosis and ADCskewness of the ASC were lower than those of the SCC. The ADCmean, ADCmax, ADCP10, ADCP25, ADCP75, ADCP90, ADCmedian, and ADCmode of AC were significantly higher than those of the SCC. The ADCP10 and ADCP10 + diameter yielded the AUCs of 0.753 and 0.778 in differentiating ASC from AC. The ADCmedian and ADCmedian + diameter yielded the AUCs of 0.807 and 0.838 in differentiating AC from SCC. The ADCskewness yielded the AUC of 0.713 in differentiating ASC from SCC. Conclusion The ADCP10 and ADCP10 + diameter, ADCmedian, and ADCmedian + diameter performed well in differentiating ASC from AC and AC from SCC, respectively. However, ADCskewness exhibited a limited ability in differentiating ASC from SCC.

A comparative study of four diffusion-weighted imaging models in the diagnosis of cervical cancer

Background Most commonly used diffusion-weighted imaging (DWI) models include intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential model (SEM), and mono-exponential model (MEM). Previous studies of the four models were inconsistent on which model was more effective in distinguishing cervical cancer from normal cervical tissue. Purpose To assess the performance of four DWI models in characterizing cervical cancer and normal cervical tissue. Material and Methods Forty-seven women with suspected cervical carcinoma underwent DWI using eight b-values before treatment. Imaging parameters, calculated using IVIM, SEM, DKI, and MEM, were compared between cervical cancer and normal cervical tissue. The diagnostic performance of the models was evaluated using independent t-test, Mann–Whitney U test, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression analysis. Results All parameters except pseudo-diffusion coefficient (D*) differed significantly between cervical cancer and normal cervical tissue ( P < 0.001). Through logistic regression analysis, all combined models showed a significant improvement in area under the ROC curve (AUC) compared to individual DWI parameters. The model with combined IVIM parameters had a larger AUC value compared to those of other combined models ( P < 0.05). Conclusion All four DWI models are useful for differentiating cervical cancer from normal cervical tissue and IVIM may be the optimal model.

The value of magnetic resonance blood oxygen level-dependent imaging in evaluating the efficacy of advanced cervical cancer combined with radiotherapy and chemotherapy

Background Blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) is an imaging method used to analyze oxygenation status of the tumor. Purpose To investigate the feasibility of BOLD-MRI in evaluating the efficacy of advanced cervical cancer combined with radiotherapy and chemotherapy. Material and Methods This prospective study included 85 patients with advanced cervical cancer who received BOLD-MRI examination before and after concurrent chemoradiotherapy from October 2020 to December 2021. To investigate the changes of baseline R2* values and △R2* values of cervical cancers before and after treatment. Results 29 cases were complete response, 34 cases were partial response, and 22 cases showed progression. The baseline R2* values of the tumors were lower than that of the normal cervical muscle ( P < 0.0001). After oxygen stimulation, the baseline R2* values of the tumors decreased ( P = 0.012). After treatment, the baseline R2* values of the tumors increased ( P = 0.007), and the dynamic △R2* values of the tumors decreased ( P = 0.025). The baseline R2* value of the complete response was the highest ( P = 0.000), the dynamic △R2* value of the complete response was the lowest ( P = 0.017). Conclusion BOLD-MRI can evaluate the efficacy of concurrent chemoradiotherapy for advanced cervical cancer.

Diagnostic value of contrast-enhanced ultrasound in ovarian cancer: a meta-analysis

Background Ovarian cancer has been reported to be the eighth most common cancer among women worldwide. Purpose To assess the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) in distinguishing between benign and malignant ovarian tumors. Material and Methods A comprehensive search of scientific literature databases, including PubMed, Cochrane Library, Web of Science, Wanfang, and CNKI, was conducted from their inception to November 2019 to identify relevant studies on the use of CEUS in the differential diagnosis of benign and malignant ovarian tumors. Sensitivity (SEN), specificity (SPE), positive and negative likelihood ratios (LR+/LR–), diagnostic odds ratios (DORs), and their corresponding 95% confidence intervals (CIs) were retrieved and analyzed using Stata 15.0. Results After rigorous screening, a total of 15 high-quality clinical studies encompassing 934 patients with ovarian cancer, comprising 969 ovarian tumors (403 malignant tumors and 566 benign tumors), were included in the analysis. Data analysis revealed significant correlations between CEUS and various diagnostic indices for ovarian tumors: the combined SEN and SPE were 0.93 (95% CI = 0.88–0.96) and 0.93 (95% CI = 0.90–0.96), respectively, and the combined LR+ and LR– were 14.07 (95% CI = 9.46–20.92) and 0.08 (95% CI = 0.05–0.13), respectively, with a combined DOR of 185.15 (95% CI = 93.31–367.41). The area under the summary receiver operating characteristic curve (AUC) was 0.98 (95% CI = 0.96–0.99). No publication bias was detected in the meta-analysis ( P  = 0.62). Conclusion CEUS demonstrates significant value in the diagnosis and differential diagnosis of benign and malignant ovarian tumors.

Comparison of the transradial and transfemoral approaches for uterine artery embolization: a randomized clinical trial

Background Uterine artery embolization (UAE) is a procedure commonly used to control uterine bleeding or pain. While the procedure is traditionally performed through the transfemoral approach (TFA), the transradial approach (TRA) is another method. Purpose To compare the effectiveness of the UAE using the TRA approach versus the TFA approach. Material and Methods This non-blinded, randomized clinical trial was conducted at a tertiary hospital between 1 January 2019 and 30 June 2022. A total of 42 female patients with abnormal uterine bleeding and/or pelvic pain from uterine fibroids were randomly assigned to either the TRA group or the TFA group. Data collected included demographic information, procedural details, patient satisfaction, and radiation metrics. Results The TRA group had significantly lower numbers of microsphere vials used compared to the TFA group ( P  = 0.014). While there were no significant differences in procedure times ( P  = 0.058), fluoroscopic times ( P  = 0.117), or radiation doses ( P  = 0.466), the TRA approach was associated with a higher success rate in achieving bilateral UA catheterization and fewer instances of bilateral sheath insertion. Patient satisfaction scores were similar between the groups, with no statistically significant difference ( P  = 0.932). Minor adverse events such as local hematoma and color changes were more frequent in the TFA group, though these differences were not statistically significant. Conclusion Although both approaches were effective for the UAE, the TRA approach may be a viable alternative to the TFA due to its higher success rate in achieving bilateral catheterization, lower radiation doses, and shorter procedural times.

Development of a nomogram based on whole-tumor multiparametric MRI histogram analysis to predict deep myometrial invasion in stage I endometrioid endometrial carcinoma preoperatively

Background The depth of myometrial invasion determines whether International Federation of Gynecology and Obstetrics stage I endometrioid endometrial carcinoma (EEC) patients undergo lymph node dissection. However, subjective evaluation results relying on magnetic resonance imaging (MRI) are not always satisfactory. Purpose To develop a nomogram based on whole-volume tumor MRI histogram parameters to preoperatively predict deep myometrial invasion (DMI) in patients with stage I EEC. Material and Methods This retrospective analysis included 131 EEC patients and a training/validation cohort of 92/39 patients at a 7:3 ratio. The histogram parameters were obtained from multiple sequences (ADC mapping and T2-weighted imaging) within volumes of interest. Univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were used for feature selection. The performance of clinical model, histogram model, and histogram nomogram was evaluated by calculating the area under the receiver operating characteristic curve (AUC). Results Age and two morphological features (maximum anteroposterior tumor diameter on sagittal T2-weighted images [APsag] and the tumor area ratio [TAR]) were selected to construct the clinical model. Five histogram parameters were selected for the creation of the histogram model. The nomogram, which combines the histogram parameters, age, APsag, and TAR, achieved the highest AUCs in both the training and validation cohorts (nomogram vs. histogram vs. clinical model: 0.973 vs. 0.871 vs. 0.934 [training] and 0.972 vs. 0.870 vs. 0.928 [validation]). Conclusion The MR histogram nomogram can help predict the DMI of patients with stage I EEC preoperatively, assisting physicians in the development of personalized treatment strategies.

Improved diagnosis of adnexal lesions by integrating intra-tumoral hemorrhage detection with non-contrast MRI scoring (NCMS) using susceptibility-weighted sequences

Background Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) protocol was included into the Ovarian-Adnexal Reporting & Data System (O-RADS) MRI scoring system. To avoid the administration of contrast medium, the non-contrast MRI scoring (NCMS) system was proposed. Purpose To evaluate the contribution of detecting intra-tumoral hemorrhage in the solid tissue of adnexal masses to improve tumor characterization and enhance the risk stratification of adnexal lesions using the NCMS system. Material and Methods MRI findings including susceptibility-weighted sequences (T2*-weighted MR angiography [SWAN]) were retrospectively analyzed in 126 surgically confirmed adnexal tumors with solid tissue components (20 benign, 106 malignant). Solid tissue was classified as malignant based on the NCMS criteria, defined by intermediate intensity on T2-weighted (T2W) imaging, and corresponding diffusion restriction. Hemorrhage was assessed based on high intensity on T1-weighted (T1W) imaging and susceptibility-related signal voids on SWAN. Results The NCMS solid tissue criteria identified malignancy with a sensitivity of 94.3%, specificity of 60%, and accuracy of 88.9%. High intensity on T1W imaging and signal voids on SWAN were observed in 23.6% and 72.6% of malignant lesions, compared to 0% and 5% in benign lesions, respectively. Hemorrhage was frequently observed in high-grade malignant tumors, or hemorrhagic subtypes. The combination of NCMS criteria and/or presence of intra-tumoral hemorrhage was associated with malignancy, yielding a sensitivity of 98.1%, specificity of 60%, and accuracy of 92.1%. Conclusion The inclusion of intra-tumoral hemorrhage enhances the diagnostic accuracy of the NCMS for characterizing adnexal lesions. SWAN may also aid in estimating tumor grade and identifying hemorrhagic subtypes.

Comparison of 68Ga-FAPI PET CT/MRI and 18F-FDG PET/CT in metastatic lesions of gynecological cancers: a systematic review and head-to-head meta-analysis

Background 68Ga-labled fibroblast activating protein inhibitor (68Ga-FAPI) represents a new and exciting positron emission tomography-computed tomography/magnetic resonance (PET-CT/MR) radiotracer. Purpose To compare the diagnostic efficacy of 68Ga-FAPI PET CT/MR and 18F-fluorodeoxyglucose (18F-FDG) PET/CT in metastatic lesions of gynecological cancers (GCs). Material and Methods The PubMed, Embase, and Web of Science databases were thoroughly investigated from inception until 22 December 2023. A head-to-head contrast between 18F-FDG PET/CT as well as 68Ga-FAPI PET CT/MR for the assessment of GCs was presented by the included studies. A random variable model was employed to examine the sensitivity in detection of lymph node (LN) and peritoneal metastases (PM). Results The pooled sensitivity for 68Ga-FAPI PET CT/MR and 18F-FDG PET/CT in lymph node metastases (LNM) of GC were 0.98 (95% confidence interval [CI] = 0.86–1) and 0.85 (95% CI = 0.65–0.98), respectively, while the results about peritoneal metastases in ovarian cancer were 0.98 (95% CI = 0.93–1) and 0.71 (95% CI = 0.55–0.86). Compared with 18F-FDG PET/CT, 68Ga-FAPI PET CT/MR exhibited a better sensitivity in peritoneal involvement of ovarian cancer with a relative risk of 0.24 (95% CI = 0.09–0.40) and P = 0.002. Conclusion 68Ga-FAPI PET CT/MR displayed a superior sensitivity over 18F-FDG PET/CT in detecting metastatic lesions of ovarian cancer. However, there was insufficient evidence to favor the superiority of 68Ga-FAPI PET CT/MR in LNM of CC. Further studies are needed for evaluating primary and metastatic lesions of 68Ga-FAPI PET CT/MR in different GC.

Prediction of postoperative residual primary ovarian neoplasm or metastatic lesion close to rectum of serous ovarian carcinoma based on clinical and MR T1-DEI features

Background The optimal primary debulking surgery outcome of serous ovarian carcinoma (SOC) is greatly affected by primary ovarian neoplasm or metastatic lesion close to the rectum. Purpose To study the risk factors affecting postoperative residual primary ovarian neoplasm or metastatic lesion close to the rectum of SOC. Material and Methods The clinical and MRI data of 164 patients with SOC eligible from institution A (training and test groups) and 36 patients with SOC eligible from institution B (external validation group) were collected and retrospectively analyzed. The clinical data included age, serum carbohydrate antigen 125 (CA-125), human epididymis protein 4, and neutrophil-to-lymphocyte ratio (NLR). Magnetic resonance imaging (MRI) data included ovarian mass distribution, maximum diameter of ovarian mass, ovarian mass features, degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion, and amount of ascites. A model was established using multivariate logistic regression. Results By univariate and multivariate logistic regressions, CA-125 ( P = 0.024, odds ratio [OR] = 3.798, 95% confidence interval [CI] = 1.24–13.32), NLR ( P = 0.037, OR = 3.543, 95% CI = 1.13–12.72), and degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion ( P < 0.001, OR = 37.723, 95% CI = 7.46–266.88) were screened as independent predictors. The area under the curve values of the model in the training, test, and external validation groups were 0.860, 0.764, and 0.778, respectively. Conclusion The clinical-radiological model based on T1-weighted dual-echo MRI can be used non-invasively to predict postoperative residual ovarian neoplasm or metastasis close to SOC in the rectum.

Nomograms combining clinical factors and apparent diffusion coefficient to predict downstaging and progression-free survival after concurrent chemoradiotherapy in patients with cervical cancer

Background Concurrent chemoradiotherapy (CCRT) is used as the primary treatment modality for currently limited cervical cancer and lacks non-invasive quantitative parameters to assess clinical outcomes of treatment for cervical cancer treatment. Purpose To develop nomograms based on clinical prognostic factors and apparent diffusion coefficient (ADC) in predicting downstaging and progression-free survival (PFS) after CCRT for cervical cancer. Material and Methods X-tile was used to calculate the optimal threshold for ΔADCmean(%) for prognostic stratification. Kaplan–Meier curves were used to calculate the difference in PFS between high- and low-risk groups. Univariate and multivariate Cox proportional risk regression models were used to identify clinical and radiological risk factors for prognosis and construct a prognostic nomogram model. Results ΔADCmean(%) was significantly correlated with tumor downstaging; the area under the receiver operating characteristic curve (AUC) was 0.868. X-tile showed that the optimal threshold for ΔADCmean(%) to diagnose prognosis was 40.8. Kaplan–Meier curves showed that the low-risk population in the training group had significantly longer PFS within 3 years ( P < 0.001). Multivariate Cox regression showed that ΔADC (%) is independent risk factor for PFS. The C-index of ΔADC(%) predicting 3-year PFS in the training set is 0.761 and the C-index of the nomogram model is 0.862. Conclusion ΔADCmean(%) is a non-invasive biomarker for predicting tumor downstaging in cervical cancer after CCRT. The nomograms based on ΔADCmean(%) predict PFS of patients with cervical cancer with moderate accuracy.

Assessment of the utility of intravoxel incoherent motion and diffusion kurtosis imaging for determining eligibility for fertility preservation

Background Accurate preoperative assessment of endometrial cancer (EC) is crucial in young women who may be eligible for fertility-preserving therapy, which is generally limited to patients with grade 1, endometrioid-type tumors without myometrial invasion (MI). Purpose To evaluate the utility of quantitative parameters derived from intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) for improving the diagnostic performance of magnetic resonance imaging (MRI). Material and Methods This retrospective study included 107 patients diagnosed with EC (mean age = 59 years; age range = 25–89 years) who underwent preoperative MRI, including multiple b-value (0–2000 s/mm 2 ) diffusion-weighted imaging, between January 2022 and March 2024. Quantitative parameters were extracted from the mono-exponential (ADC), IVIM (Di, D*, f), and DKI (Dk, K) models and compared across clinical and pathological features. Results ADC, Di, and Dk values were significantly higher in patients without MI ( P  = 0.015, 0.035, and 0.005, respectively). Di and Dk were significantly higher ( P  = 0.003 and 0.016), and K was significantly lower ( P  = 0.013) in the G1 group. Patients eligible for fertility preservation had significantly higher ADC, Di, and Dk values ( P  = 0.002, 0.002, and 0.001) and significantly lower K values ( P  = 0.044). The overall diagnostic performance of these parameters was moderate (area under the curve < 0.70). Conclusion IVIM and DKI-derived metrics may enhance preoperative assessment of tumor grade and MI, supporting decisions regarding fertility-preserving treatment.

The value of multimodal functional magnetic resonance imaging in differentiating p53abn from p53wt endometrial carcinoma

Background Endometrial carcinoma (EC) is the sixth most common cancer in women. P53 gene expression in patients with endometrial cancer can predict the efficacy and prognosis of patients with neoadjuvant therapy. Purpose To explore the value of multimodal magnetic resonance imaging (MRI) in differentiating p53 abnormal (p53abn) from p53 wild-type (p53wt) EC. Material and Methods Data from 47 EC patients, including 14 p53abn cases and 33 p53wt cases, were retrospectively analyzed. The preoperative MRI sequences included amide proton transfer weighted (APTw) imaging, T2 mapping, mDIXON-Quant imaging and diffusion-weighted imaging (DWI). After post-processing, APT, T2, transverse relaxation rate (R2*), fat fraction (FF) and apparent diffusion coefficient (ADC) maps were obtained. The APT, T2, R2*, FF and ADC values for lesions of the two groups of cases were measured by two observers who were blind to the pathological data. Results The APT value and R2* value in the p53abn group were higher than those in the p53wt group, while the ADC value was lower (all P < 0.05). There was no statistically significant difference in T2 value and FF value between the two groups (all P > 0.05). The area under curve of APT, R2*, ADC and combined APT + R2*+ADC values for identification of p53abn and p53wt EC were 0.739, 0.689, 0.718 and 0.820, respectively (all P > 0.05). Conclusion APTw, mDIXON-Quant and DWI techniques can be usedfor quantitative identification of p53abn and p53wt EC. The multimodal MRI provides a new way for preoperative quantitative evaluation of EC molecular typing, which has certain clinical application value.

Evaluation of lymphovascular space invasion in endometrial carcinoma by APTw and mDixon-Quant

Background Lymphovascular space invasion (LVSI) is a strong and independent risk factor that increases the probability of endometrial carcinoma (EC) recurrence and reduces the survival rate of patients. Purpose To investigate the value of amide proton transfer weighted (APTw) and mDixon-Quant techniques in evaluating EC lymphovascular space invasion (LVSI). Material and Methods Data of 50 EC patients (18 LVSI+ and 32 LVSI–) confirmed by surgery and pathology were retrospectively analyzed. Preoperative magnetic resonance imaging (MRI) scans included APTw and mDixon-Quant imaging. APT, transverse relaxation rate (R2*), and fat fraction (FF) plots were obtained by postprocessing. The APT, R2*, and FF values of the two groups of cases were measured by two observers. Results The agreement between the two observers was good. The mean APT, R2*, and FF values of LVSI+ EC were 2.947% ± 0.399%, 20.605 /s (range = 18.525–27.953), and 2.234% ± 1.047%, respectively, while the parameters of LVSI– EC were 2.628% ± 0.307%, 18.968 /s (range = 16.225–20.544), and 2.103% ± 1.070%, respectively. The APT and R2* values of LVSI+ EC were higher than those of LVSI– EC ( P < 0.05). There was no significant difference in FF value between the two groups. The AUC values of APT, R2*, and APT + R2* for LVSI were 0.751, 0.713, and 0.781, respectively (all P > 0.05). APT value was moderately correlated with R2* value (r = 0.528, P < 0.001) and weakly correlated with FF value ( r = 0.312, P = 0.027). Conclusion APTw and mDixon-Quant techniques could evaluate the LVSI status of EC, and their combined application could improve diagnostic efficiency.

MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study

Background Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs). Purpose To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting. Material and Methods Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1–3 were utilized for training and validation, and the images from devices 4–7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models. Results The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists. Conclusion Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.

Diagnostic accuracy of MRI for assessing lymphovascular space invasion in endometrial carcinoma: a meta-analysis

Background The lymphovascular space invasion (LVSI) status of endometrial cancer (EC) has guiding significance in lymph node dissection. However, LVSI can only be obtained after surgery. Researchers have tried to extract the information of LVSI using magnetic resonance imaging (MRI). Purpose To evaluate the ability of preoperative MRI to predict the LVSI status of EC. Material and Methods A search was conducted by using the PubMed/MEDLINE, EMBASE, Web of Science, and the Cochrane Library databases. Articles were included according to the criteria. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2. A bivariate random effects model was used to obtain pooled summary estimates, heterogeneity, and the area under the summary receiver operating characteristic curve (AUC). A subgroup analysis was performed to identify sources of heterogeneity. Results A total of nine articles (814 patients) were included. The risk of bias was low or unclear for most studies, and the applicability concerns were low or unclear for all studies. The summary AUC values as well as pooled sensitivity and specificity of LVSI status in EC were 0.82, 73%, and 77%, respectively. According to the subgroup analysis, radiomics/non-radiomics features, country/region, sample size, age, MR manufacturer, magnetic field, scores of risk bias, and scores of applicability concern may have caused heterogeneity. Conclusion Our meta-analysis showed that MRI has moderate diagnostic efficacy for LVSI status in EC. Large-sample, uniformly designed studies are needed to verify the true value of MRI in assessing LVSI.

Endometrial cancer: the role of MRI quantitative assessment in preoperative staging and risk stratification

Background New methods to reduce subjectivity in preoperative magnetic resonance imaging (MRI) staging of endometrial cancer are needed. Purpose To investigate the role of MRI quantitative assessment in staging and risk stratification of endometrial cancer. Material and Methods Preoperative T2-weighted (T2W) images and diffusion-weighted imaging of 42 patients were analyzed retrospectively by two radiologists. Tumor area ratio (TAR) and tumor volume ratio (TVRseg) were calculated by semi-automatic segmentation of the tumor and uterus on T2W imaging and apparent diffusion coefficient (ADC). TVR was also calculated by the 3D metric method (TVRmetric). Mean ADCtumor was calculated. The patients were allocated to risk groups regarding the stage, grade, and lymphovascular invasion (LVI) status. Results TAR, TVRmetric, T2W TVRseg, and ADC TVRseg showed a significant difference between the superficial and deep myometrial invasion groups ( P < 0.001). All of these parameters showed a good diagnostic performance for detecting deep myometrial invasion (AUC>0.82), the highest accuracy rate (85%) was found with T2W TVRseg. LVI was significantly associated with TAR ( P = 0.002) and T2W TVRseg ( P = 0.014), while the cervical invasion was associated with TAR ( P = 0.03). ADCtumor was significantly lower in high-grade tumors ( P = 0.002). There was a significant difference in ADCtumor ( P = 0.002), TAR ( P = 0.004), and T2W TVRseg ( P = 0.038) between the low- and high-risk groups. AUC of TAR and T2W TVRseg for detecting high-risk groups were 0.80 and 0.77, respectively, while AUC of ADCtumor for the low-risk group was 0.75. Conclusion MRI quantitative assessments such as TAR, TVR, and ADCtumor may improve the accuracy of preoperative staging and can help in risk stratification of endometrial cancer.

The value of the apparent diffusion coefficient in differentiating type II from type I endometrial carcinoma

Background Diagnostic type II endometrial carcinoma (EC) is considered more aggressive and has a poorer prognosis than type I EC; differentiation between them is helpful for preoperative clinical decision-making. However, the diagnostic value of the apparent diffusion coefficient (ADC) in differentiating them remains unclear. Purpose To investigate the value of ADC in differentiating type II EC from type I EC. Material and Methods Ninety-four patients with EC who underwent diffusion-weighted imaging (DWI) were retrospectively included and divided into type I and type II subgroups, based on the postoperative pathologic results. We analyzed the clinical characteristics, conventional magnetic resonance imaging manifestations, and ADC mean values (ADCmean), ADC minimum values (ADCmin), and ADC max values (ADCmax). Receiver operating characteristic (ROC) curve analysis was further used to assess the predictive performance. Results The ADCmean, ADCmin, and tumor size differed significantly between the two subtypes. The area under the ROC curve (AUC) for ADCmean and ADCmin was 0.787 (95% confidence interval [CI] = 0.692–0.88) and 0.835 (95% CI = 0.751–0.919) for predicting type II EC, respectively. The optimal cut-off value of ADCmean for prediction was 0.757 × 10–3 mm2/s with a sensitivity of 91%, a specificity of 58%, and an accuracy of 74%, while for ADCmin was 0.637 × 10–3 mm2/s with a sensitivity of 82%, a specificity of 73%, and an accuracy of 75%. Conclusion EC with lower ADCmean and ADCmin values derived from DWI, and a larger size, are indicative of type II EC.

The prominent value of apparent diffusion coefficient in assessing high-risk factors and prognosis for patients with endometrial carcinoma before treatment

Background To date, there are no consensus methods to evaluate the high-risk factors and prognosis for managing the personalized treatment schedule of patients with endometrial carcinoma (EC) before treatment. Apparent diffusion coefficient (ADC) is regarded as a kind of technique to assess heterogeneity of malignant tumor. Purpose To explore the role of ADC value in assessing the high-risk factors and prognosis of EC. Material and Methods A retrospective analysis was made on 185 patients with EC who underwent 1.5-T magnetic resonance imaging (MRI). Mean ADC (mADC), minimum ADC (minADC), and maximum ADC (maxADC) were measured and compared in different groups. Results Among the 185 patients with EC, the mADC and maxADC values in those with high-risk factors (type 2, deep myometrial invasion, and lymph node metastasis) were significantly lower than in those without. According to receiver operating characteristic (ROC) curve analysis, the areas under the curve (AUC) were significant for mADC, minADC, and maxADC predicting high-risk factors. Furthermore, the AUCs were significant for mADC and maxADC predicting lymph node metastasis but were not significant for minADC. Patients with lower mADC were associated with worse overall survival and disease-free survival; the opposite was true for patients with higher mADC. Conclusion Our study showed that ADC values could be applied to assess the high-risk factors of EC before treatment and might significantly relate to the prognosis of EC. It might contribute to managing initial individualized treatment schedule and improve outcome in patients with EC.

Is the standard deviation of the apparent diffusion coefficient a potential tool for the preoperative prediction of tumor grade in endometrial cancer?

Background The tumor histological grade is closely related to the prognosis of endometrial cancer (EC). The use of the apparent diffusion coefficient (ADC), tumor volume, and MRI-based texture analysis has allowed exciting advances in predicting EC grade before surgery. However, whether this constitutes a simple, convenient, and powerful diagnostic method remains unknown. Purpose To explore the utility of standard deviation (SD) of the ADC (ADCSD) for predicting the tumor grade in patients with EC. Material and Methods We retrospectively evaluated 138 patients with EC. All patients underwent unenhanced MRI and diffusion-weighted imaging (DWI). The mean ADC value (ADCmean) and SD were obtained using a freehand region of interest traced on the ADC map. Spearman’s linear correlation coefficients were calculated to analyze the correlations between the indexes (including ADCSD and the ADCmean) and the Ki-67 index. The Kruskal–Wallis and Mann–Whitney U tests were used to compare differences in the index results among tumor grades. Results A significant difference in ADCSD was observed among the tumor grades ( P=0.000), and the ADCSD value was significantly higher for high-grade EC than for low-grade tumors (289.7 vs. 216.3×10−6mm2 /s, P=0.000). A statistically significant positive correlation was observed between ADCSD and the Ki-67 index (r=0.364, P=0.000). According to the receiver operating characteristic curve, ADCSD ≥240.2×10−6mm2 /s predicted high-grade EC with a sensitivity, specificity, and accuracy of 73.1%, 80.2%, and 77.5%, respectively. Conclusion Based on the intratumor heterogeneity of EC, ADCSD represents a potential method for the preoperative prediction of high-grade EC, although further studies are needed.

Diagnostic value of diffusion-weighted magnetic resonance imaging in discriminating between metastatic and non-metastatic pelvic lymph nodes in endometrial cancer

Background Researchers have recently focused on assessing the accuracy of diffusion-weighted magnetic resonance imaging (DW-MRI) in predicting pelvic lymph node metastases in gynecological malignancies. Purpose To evaluate the diagnostic value of DW-MRI in discriminating between metastatic and non-metastatic pelvic lymph nodes in endometrial cancer patients. Material and Methods This retrospective database study was conducted with 33 women aged 30–84 years with pathologically proven endometrial cancer that had been assessed by DW-MRI before their first treatment initiation at our referral hospital from March 2016 to April 2019. The diffusion technique (b = 50, 400, and 1000 mm2/s) was used in the imaging, and continuous apparent diffusion coefficient (ADC) metrics (ADCmin, ADCmax, ADCmean, ADCSD, and rADC) were compared between the metastatic and non-metastatic lymph nodes. Results In total, 48 lymph nodes from 33 patients were assessed. All metastatic lymph nodes were restricted, while among the non-metastatic lymph nodes, only 19.3% were restricted. Considering pathological reports of metastatic and non-metastatic lymph nodes as the gold standard, DWI-related restricted and non-restricted features had a sensitivity of 80.6%, a specificity of 100%, and an accuracy of 87.5% to discriminate between a metastatic and non-metastatic pattern. ADC metrics of ADCmin, ADCmax, ADCmean, ADCSD, and rADC showed high values enabling differentiation between metastatic and non-metastatic lymph nodes. The best cut-off values were 0.7 × 10−3, 1.2 × 10−3, 1.01 × 10−3, 123, and 0.78, respectively. Conclusion DW-MRI is a useful quantitative tool for differentiating between metastatic and benign lymph nodes in endometrial cancer patients.

The apparent diffusion coefficient (ADC) on 3-T MRI differentiates myometrial invasion depth and histological grade in patients with endometrial cancer

Background Diffusion-weighted magnetic resonance imaging (DW-MRI) with apparent diffusion coefficient (ADC) measurement provides additional information about tumor microstructure with potential relevance for staging and predicting aggressive disease in patients with endometrial cancer (EC). Purpose To determine whether ADC values in EC diverge according to the tumor’s histologic grade and myometrial invasion depth. Material and Methods A sample of 48 pathologically confirmed cases of EC were reviewed retrospectively. The sample was distributed as follows: G1 (n = 9); G2 (n = 18); G3 (n = 21); with myometrial invasion <50% (n = 31); and with myometrial invasion ≥50% (n = 17). DW images were performed at 3.0T with b factors of 0–1000/mm2. The region of interest (ROI) was defined within the tumor with T1-weighted and T2-weighted imaging and copied manually to an ADC map. The tumor’s grade and myometrial invasion’s depth were determined by postoperative histopathological tests. Results The means of ADCmin and ADCmean values were significantly lower for patients with G2 and G3 endometrial tumors than G1. The same tendency was observed in myometrial invasion, as both ADCmin and ADCmean values were lower for patients with deep than for those with superficial myometrial invasion. The cut-off values of the ADCmin and ADCmean that predicted high-grade tumors were 0.69 × 10−3 mm2/s and 0.82 × 10−3 mm2/s, respectively, while those for myometrial infiltration were 0.70 × 10−3 mm2/s (ADCmin) and 0.88 × 10−3 mm2/s (ADCmean). Conclusion ADCmin and ADCmean values correlated with histologic tumor grade and myometrial invasion depth; therefore, it is suggested that ADC on MRI may be a useful indicator to predict malignancy of ECs.

Apparent diffusion coefficient (ADC) values of serous, endometrioid, and clear cell carcinoma of the ovary: pathological correlation

Background Primary epithelial ovarian cancer is divided into several subtypes. The relationships between apparent diffusion coefficient (ADC) values and their subtypes have not yet been established. Purpose To investigate whether ADC values of epithelial ovarian cancer vary according to histologic tumor cellularity and evaluate the difference of clear cell carcinoma (CCC), high-grade serous carcinoma (HGSC), and endometrioid carcinoma (EC). Material and Methods This retrospective study included 51 cases of epithelial ovarian cancer (17 CCC, 20 HGSC, and 14 EC) identified by magnetic resonance imaging with pathological confirmation. All patients underwent diffusion-weighted imaging and the ADC values of lesions were measured. We counted the tumor cells in three high-power fields and calculated the average for each case. The Spearman’s correlation coefficient test was used to analyze correlation between ADC values and tumor cellularity. The ADC values of HGSC, EC, and CCC were compared using the Steel–Dwass test. Results The ADC values of all cases were significantly inversely correlated with tumor cellularity ( rs = −0.761; P < 0.001). The mean ± SD ADC values (×10−3 mm2/s) of CCC, HGSC, and EC were 1.24 ± 0.17 (range 0.98--1.65), 0.84 ± 0.10 (range 0.67--1.06), and 0.84 ± 0.10 (range 0.67--1.07). The mean ± SD tumor cellularity of CCC, HGSC, and EC was 162.88 ± 63.28 (range 90.33--305.67), 440.60 ± 119.86 (range 204.67--655.67), and 461.02 ± 81.86 (range 333.33--602.33). Conclusion There is a significant inverse correlation between ADC values and tumor cellularity in epithelial ovarian cancer. The mean ADC value of CCC was higher than those of HGSC and EC, seemingly due to the low cellularity of CCC.

MR volumetry in predicting the aggressiveness of endometrioid adenocarcinoma: correlation with final pathological results

Background Magnetic resonance (MR) has been widely used in predicting the aggressiveness of endometrioid adenocarcinoma. However, the diagnostic value of the MR volume of the lesion has been controversial. Purpose To determine whether the whole-lesion MR volume measurement could be used as a better predictor for evaluating the aggressiveness of endometrioid adenocarcinoma. Material and Methods In this retrospective study, we include 357 patients with pathologically demonstrated endometrioid adenocarcinoma at our institution between 1 January 2013 and 31 December 2018. Whole-lesion MR volume was calculated on sagittal T2-weighted images with ITK-SNAP software on a personal computer. Results According to the receiver operating characteristics curve analysis, whole-lesion MR volume has the competitive advantage in evaluating deep myometrial invasion compared with the frozen results, generating area under the curve (AUC) values of 0.751 vs. 0.834 ( P = 0.0629, Z = 1.860). The AUC of tumor maximum diameter, simple tumor volume, and whole-lesion MR volume in predicting deep myometrial invasion was 63.8%, 67.6%, and 75.1%, respectively. Conclusion Whole-lesion MR volume is a good diagnostic tool for prediction of deep myometrial invasion, lymph node metastasis, and lymphovascular invasion. MR volumetry could reflect the aggressiveness of endometrioid adenocarcinoma more accurately than traditional lesion measurements.

DTI histogram parameters correlate with the extent of myoinvasion and tumor type in endometrial carcinoma: a preliminary analysis

Background Myoinvasion and tumor-type determines surgical planning in endometrial carcinoma. Purpose To evaluate whole tumor diffusion tensor imaging histogram texture parameters in evaluating myoinvasion and tumor type in endometrial carcinoma. Material and Methods Twenty-seven patients with endometrial carcinoma underwent diffusion tensor imaging on a 1.5-T MRI system using echo-planar imaging sequence with 0 and 700 s/mm2 b values. Whole tumor histogram parameters were obtained from fractional anisotropy, mean diffusivity maps. Mann–Whitney U test and receiver operating characteristic curve analyses were used Results The mean fractional anisotropy of tumors with no myoinvasion was significantly higher than tumors which underwent myoinvasion, suggesting higher anisotropy in tumors which did not invade the myometrium. Voxel-wise heterogeneity in distribution of fractional anisotropy and mean diffusivity was seen in the form of higher uniformity and lower entropy of tumors with superficial <50% myoinvasion versus >50% myoinvasion. Uniformity, entropy, and energy of voxel-wise fractional anisotropy distribution gave an area under the curve of 0.827, 0.821, and 0.796, respectively, in predicting the presence of deep myometrial invasion while energy, entropy, and uniformity of mean diffusivity distribution in tumor gave an area under the curve of 0.84, 0.815, and 0.809 respectively. Tumor type was predicted with an area under the curve of 0.747, 0.759, and 0.765 for the uniformity, energy, and entropy of voxel-wise fractional anisotropy distribution. A logistic regression combining all the important histogram parameters obtained 94% and 88% sensitivity and 88% and 80% specificity in predicting deep myoinvasion and tumor type, respectively. Conclusion Diffusion tensor histogram analysis can better characterize endometrial carcinomas and can be used as a quantitative marker of tumor behavior.

Magnetic resonance imaging findings of extrauterine high-grade serous carcinoma based on new pathologic criteria for primary site assignment

Background There has been no study that has reported magnetic resonance imaging (MRI) findings of extrauterine high-grade serous carcinomas (HGSCs) that have been histologically determined by the new criteria. Purpose To assess MRI findings of extrauterine HGSCs based on new pathologic criteria. Material and Methods Fifty patients with histopathologically proven extrauterine HGSCs, who underwent pretreatment gadolinium-enhanced MRI, were included in this study. After surgery, the primary sites were histopathologically determined based on new criteria for primary site assignment in extrauterine HGSCs as follows: fallopian tube (n = 34); ovary (n = 9); primary peritoneal HGSC (n = 1); and tubo-ovarian (n = 6). We retrospectively reviewed MR images and compared the MR findings between tubal and ovarian primaries. Results MRI patterns with tubal primaries were classified as ovarian cancer (62%), peritoneal cancer (35%), and fallopian tube cancer (3%). MRI patterns with ovarian primaries were classified as ovarian cancer (78%) and peritoneal cancer (22%). The frequency of the involvement of the fallopian tube, ovary, peritoneum, uterus, and lymph node was not significantly different between the two pathologies. There was no significant difference in the abnormal amount of ascites, hemorrhagic ascites, or characteristics of the ovarian lesions between the two pathologies. Conclusion On MR images, tubal primaries almost always exhibited ovarian or peritoneal cancer pattern, but rarely exhibited fallopian tube cancer pattern. MR findings could not accurately differentiate between tubal and ovarian primaries; therefore, histopathologic investigation is essential for determination of the primary site of extrauterine HGSCs.

MRI quantitative parameters combined with pathological microvascular characteristics predict lymph node metastasis of cervical squamous cell carcinoma

Background Lymph node metastasis (LNM) greatly affects the prognosis and treatment of cervical squamous cell carcinoma (CSCC). Non-invasive imaging biomarkers that reflect tumor angiogenesis and vascular maturity may help predict LNM. Purpose To determine whether the magnetic resonance imaging (MRI) quantitative parameters combined with microvascular characteristics predict the potential of LNM by reflecting angiogenesis or vascular maturation in CSCC. Material and Methods The clinicopathological characteristics, microvascular characteristics and MRI quantitative parameters of the LNM group (43 cases) and the non-LNM group (42 cases) were analyzed. The correlation between microvascular and MRI quantitative parameters and the ability of combined diagnosis of LNM were analyzed. Results There were significant differences in the FIGO stage and the short diameter of the largest lymph node between patients with or without LNM. There was no significant difference in the apparent diffusion coefficient (ADC) value of the primary tumor between the LNM group and the non-LNM group ( P  >0.05). Spearman’s correlation analysis showed that there was no correlation between ADC and MVD or microvessel pericyte coverage index (MPI) (all P  >0.05). K trans and K ep were significantly higher, and MPI was significantly lower in patients with LNM. There were negative correlations between K trans and MPI, and K ep and MPI. Binary logistic regression analysis showed that a combined prediction model constructed by K trans , K ep , and MPI had the highest diagnostic efficacy. Conclusion K trans and K ep of CSCC can predict LNM by non-invasively reflecting the maturity of tumor vessels, and the combined K trans , K ep , and MPI have promising diagnostic efficiency for LNM.

Intratumoral and peritumoral radiomics based on super-resolution T2-weighted imaging for prediction of normal-sized lymph node metastasis in cervical cancer

Background Preoperative identification of normal-sized lymph node metastases (LNM) remains clinically significant yet challenging in cervical cancer. Purpose To investigate the value of super-resolution T2WI-derived intratumoral and peritumoral radiomics for normal-sized LNM prediction in cervical cancer. Material and Methods A total of 257 patients from three sites of our hospital were divided into a development cohort (site 1, n = 97), a validation cohort (site 1, n = 42), and two internal test cohorts (site 2, n = 62; site 3, n = 56). Super-resolution reconstruction based on generative adversarial network was applied to all images. The volume of interest delineation encompassed primary tumor boundaries with outward expansions (1–5 mm increments) in super-resolution T2-weighted (T2W) imaging. Radiomics features were independently extracted from intratumoral and five peritumoral regions. The clinical, radiomics and combined models were built using multilayer perceptron. Model performance was evaluated through receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Results The IntraPeri3 mm radiomics model achieved superior discriminative performance compared to other radiomics models. The combined model integrated clinical variables (tumor size and squamous cell carcinoma antigen), intratumoral and peritumoral 3 mm radiomics features yielded optimal performance (AUC = 0.838 in the development cohort, 0.808 in the validation cohort, and 0.769 and 0.766 in the internal test cohorts). DCA confirmed the combined model's enhanced clinical utility across probability thresholds. Conclusion Super-resolution T2W-based radiomics aids in predicting normal-sized LNM in cervical cancer, especially the combined model incorporating clinical information, intratumoral and peritumoral 3 mm radiomics features demonstrates optimal diagnostic performance.

Application of convolutional neural network for differentiating ovarian thecoma-fibroma and solid ovarian cancer based on MRI

Background Ovarian thecoma-fibroma and solid ovarian cancer have similar clinical and imaging features, and it is difficult for radiologists to differentiate them. Since the treatment and prognosis of them are different, accurate characterization is crucial. Purpose To non-invasively differentiate ovarian thecoma-fibroma and solid ovarian cancer by convolutional neural network based on magnetic resonance imaging (MRI), and to provide the interpretability of the model. Material and Methods A total of 156 tumors, including 86 ovarian thecoma-fibroma and 70 solid ovarian cancer, were split into the training set, the validation set, and the test set according to the ratio of 8:1:1 by stratified random sampling. In this study, we used four different networks, two different weight modes, two different optimizers, and four different sizes of regions of interest (ROI) to test the model performance. This process was repeated 10 times to calculate the average performance of the test set. The gradient weighted class activation mapping (Grad-CAM) was used to explain how the model makes classification decisions by visual location map. Results ResNet18, which had pre-trained weight, using Adam and one multiple ROI circumscribed rectangle, achieved best performance. The average accuracy, precision, recall, and AUC were 0.852, 0.828, 0.848, and 0.919 ( P < 0.01), respectively. Grad-CAM showed areas associated with classification appeared on the edge or interior of ovarian thecoma-fibroma and the interior of solid ovarian cancer. Conclusion This study shows that convolution neural network based on MRI can be helpful for radiologists in differentiating ovarian thecoma-fibroma and solid ovarian cancer.

The value of amide proton transfer imaging combined with serum CA125 levels in predicting lymph vascular invasion in cervical cancer before surgery

Background Preoperative prediction of lymphovascular space invasion (LVSI) is crucial for improving the prognosis of patients with cervical cancer. Purpose To evaluate the value of preoperative amide proton transfer (APT) imaging combined with serum CA125 levels for predicting LVSI in cervical cancer. Material and Methods This retrospective study included 80 patients with cervical cancer who underwent preoperative magnetic resonance imaging, including APT imaging. Serum CA125 levels were measured using a fully automated immunoassay analyzer and chemiluminescence method. The presence of LVSI was determined based on the pathological results after surgery. Results Among the 40 patients who met the requirements, 29 had postoperative pathological confirmation of LVSI, while 11 did not. The areas under the receiver operating characteristic curves (AUC) of preoperative APT and CA125 levels predicting LVSI were 0.889 and 0.687, respectively. When the APT value was 2.9%, the corresponding Youden index was the highest (0.702), with a sensitivity of 79.3% and specificity of 90.9%. When the critical value of the preoperative serum CA15 level was 25.3 u/mL, the corresponding Youden index was the highest (0.508), with a sensitivity of 69.0% and a specificity of 81.8%. The sensitivity and specificity of preoperative APT imaging combined with serum CA125 in predicting LVSI were 82.7% and 100%, respectively, with a Youden's index of 0.828 and an AUC of 0.923. Conclusion The combination of preoperative APT imaging and serum CA125 levels is valuable for predicting LVSI in cervical cancer. Diagnostic efficacy is highest when the APT value is >2.9% and the serum CA125 level is >25.3 u/mL.

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

Background Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors. Purpose To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC). Material and Methods In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models’ performance. The calibration analysis and decision curve analysis (DCA) were also performed. Results The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram. Conclusion CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

A multicenter study of cervical cancer using quantitative diffusion-weighted imaging

Background Parameters from diffusion-weighted imaging (DWI) have been increasingly used as imaging biomarkers for the diagnosis and monitoring of treatment responses in cancer. The consistency of DWI measurements across different centers remains uncertain, which limits the widespread use of quantitative DWI in clinical settings. Purpose To investigate the consistency of quantitative metrics derived from DWI between different scanners in a multicenter clinical setting. Material and Methods A total of 193 patients with cervical cancer from four scanners (MRI1, MRI2, MRI3, and MRI4) at three centers were included in this retrospective study. DWI data were processed using the mono-exponential and intravoxel incoherent motion (IVIM) model, yielding the following parameters: apparent diffusion coefficient (ADC); true diffusion coefficient (D); pseudo-diffusion coefficient (D*); perfusion fraction (f); and the product of f and D* (fD*). Various parameters of cervical cancer obtained from different scanners were compared. Results The parameters D and ADC derived from MRI1 and MRI2 were significantly different from those derived from MRI3 or MRI4 ( P <0.01 for all comparisons). However, there was no significant difference in cervical cancer perfusion parameters (D* and fD*) between the different scanners ( P >0.05). The P values of comparisons of all DWI parameters (D, D*, fD*, and ADC) between MRI3 and MRI4 (same vendor in different centers) for cervical cancer were all >0.05, except for f ( P = 0.05). Conclusion Scanners of the same model by the same vendor can yield close measurements of the ADC and IVIM parameters. The perfusion parameters showed higher consistency among the different scanners.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance

Background The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition. Purpose To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity. Methods This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images. Results In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging ( P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging ( P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma. Conclusions DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.

Preoperative prediction of lymphovascular space invasion in endometrioid adenocarcinoma: an MRI-based radiomics nomogram with consideration of the peritumoral region

Background Lymphovascular space invasion (LVSI) of endometrial cancer (EC) is a postoperative histological index, which is associated with lymph node metastases. A preoperative acknowledgement of LVSI status might aid in treatment decision-making. Purpose To explore the utility of multiparameter magnetic resonance imaging (MRI) and radiomic features obtained from intratumoral and peritumoral regions for predicting LVSI in endometrioid adenocarcinoma (EEA). Material and Methods A total of 334 EEA tumors were retrospectively analyzed. Axial T2-weighted (T2W) imaging and apparent diffusion coefficient (ADC) mapping were conducted. Intratumoral and peritumoral regions were manually annotated as the volumes of interest (VOIs). A support vector machine was applied to train the prediction models. Multivariate logistic regression analysis was used to develop a nomogram based on clinical and tumor morphological parameters and the radiomics score (RadScore). The predictive performance of the nomogram was assessed by the area under the receiver operator characteristic curve (AUC) in the training and validation cohorts. Results Among the features obtained from different imaging modalities (T2W imaging and ADC mapping) and VOIs, the RadScore had the best performance in predicting LVSI classification (AUC train  = 0.919, and AUC validation  = 0.902). The nomogram based on age, CA125, maximum anteroposterior tumor diameter on sagittal T2W images, tumor area ratio, and RadScore was established to predict LVSI had AUC values in the training and validation cohorts of 0.962 (sensitivity 94.0%, specificity 86.0%) and 0.965 (sensitivity 90.0%, specificity 85.3%), respectively. Conclusion The intratumoral and peritumoral imaging features were complementary, and the MRI-based radiomics nomogram might serve as a non-invasive biomarker to preoperatively predict LVSI in patients with EEA.

Development of a radiomic model for cervical cancer staging based on pathologically verified, retrospective metastatic lymph node data

Background Cervical cancer is a major cause of morbidity and mortality among gynecological malignancies. Diagnostic imaging of lymph node (LN) metastasis for prognosis and staging is used; however, the accuracy in classifying the stage needs to improve. Purpose To examine the accuracy of AI-based radiomics in diagnosis, prognosis assessment and predicting the diagnostic value of radiomics for pelvic LN metastasis in cervical cancer patients. Material and Methods The study included 118 female patients with 660 LNs and 118 merged LNs. Four imaging histology models—decision tree, random forest, logistic regression, and support vector machine (SVM)—were created in this study. The imaging histology features were extracted from both the independent and merged LN groups. The AUC values for the test sets and the training sets of the four imaging histology models were compared for the independent LN group and the merged LN group. The DeLong test was used to compare the models. Result The imaging histology prediction model developed in the merged LN group outperformed the independent LN group in terms of test set AUC (0.668 vs. 0.535 for decision tree, 0.841 vs. 0.627 for logistic regression, 0.785 vs. 0.637 for random forest, 0.85 vs. 0.648 for SVM) and accuracy (0.754 vs. 0.676 for decision tree, 0.780 vs. 0.671 for random forest, 0.848 vs. 0.685 for logistic regression, 0.822 vs. 0.657 for SVM). Conclusion The constructed SVM imaging histology model for the merged LN group might be advantageous in predicting pelvic LN metastasis in cervical cancer.

Diffusion-weighted imaging in the assessment of cervical cancer: comparison of reduced field-of-view diffusion-weighted imaging and conventional techniques

Background Cervical cancer (CC) is the second most common cancer in women worldwide. Diffusion-weighted imaging (DWI) plays an important role in the diagnosis of CC, but the conventional techniques are affected by many factors. Purpose To compare reduced-field-of-view (r-FOV) and full-field-of-view (f-FOV) DWI in the diagnosis of CC. Material and Methods Preoperative magnetic resonance imaging (MRI) with r-FOV and f-FOV DWI images were collected. Two radiologists reviewed the images using a subjective 4-point scale for anatomical features, magnetic susceptibility artifacts, visual distortion, and overall diagnostic confidence for r-FOV and f-FOV DWI. The objective features included the region of interest (ROI) signal intensity of the cervical lesion (SIlesion) and gluteus maximus muscle (SIgluteus), standard deviation of the background noise (SDbackground), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The differences of measured apparent diffusion coefficient (ADC) values between the two examinations in pathological grades and FIGO tumor stages were compared. Results A total of 200 patients were included (170 with squamous cell carcinoma and 30 with adenocarcinoma). The scores of anatomical features, magnetic susceptibility artifacts, visual distortion, and overall diagnostic confidence for r-FOV DWI were significantly higher than those for f-FOV DWI. There was no difference in SNR and CNR between r-FOV DWI and f-FOV DWI. There were significant differences in ADC values between the two groups in all comparisons ( P < 0.05). Conclusion Compared with f-FOV DWI, r-FOV DWI might provide clearer imaging, fewer artifacts, less distortion, and higher image quality for the diagnosis of CC and might assist in the detection of CC.

Multidetector computed tomography in diagnosing peritoneal metastases in ovarian carcinoma

Multidetector computed tomography (MDCT) of the abdomen is currently the imaging examination of choice for the staging and follow-up of ovarian carcinoma (OC). Peritoneal metastases (PMs) represent the most common pathway for the metastatic spread of OC. MDCT scanners, due to several advantages—including increased volume coverage, reduced scanning time, acquisition of thin slices and creation of multiplanar reformations, and three-dimensional reconstructions—provide useful information regarding the early and accurate detection of PMs. Detailed mapping of peritoneal carcinomatosis is feasible, with improved detection of sub-centimeter peritoneal implants and thorough evaluation of curved peritoneal surfaces.

Diagnostic performance of dynamic contrast-enhanced magnetic resonance imaging for malignant ovarian tumors: a systematic review and meta-analysis

Background Accurate preoperative diagnosis of malignant ovarian tumors (MOTs) is particularly important for selecting the optimal treatment strategy and avoiding overtreatment. Purpose To evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for MOTs. Material and Methods A systematic search was performed in PubMed, Embase, the Cochrane Library, and Web of Science databases to find relevant original articles up to October 2019. The included studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Studies on the diagnosis of MOTs with quantitative or semi-quantitative DCE-MRI were analyzed separately. The bivariate random-effects model was used to assess the diagnostic authenticity. Meta-regression analyses were performed to analyze the potential heterogeneity. Results For semi-quantitative DCE-MRI, the pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, diagnostic odds ratio (DOR), and the area under the summary receiver operating characteristic curves (AUC) were 85% (95% confidence interval [CI] 0.75–0.92), 85% (95% CI 0.77–0.91), 5.8 (95% CI 3.8–8.8), 0.17 (95% CI 0.10–0.30), 33 (95% CI 18–61), and 0.92 (95% CI 0.89–0.94), respectively. For quantitative DCE-MRI, the pooled sensitivity, specificity, positive LR, negative LR, DOR, and AUC were 88% (95% CI 0.65–0.96), 93% (95% CI 0.78–0.98), 12.3 (95% CI 3.4–43.9), 0.13 (95% CI 0.04–0.45), 91 (95% CI 10–857), and 0.96 (95% CI 0.94–0.98), respectively. Conclusion DCE-MRI has great diagnostic value for MOTs. Semi-quantitative DCE-MRI may be a relatively mature approach; however, quantitative DCE-MRI appears to be more promising than semi-quantitative DCE-MRI.

External validation of Risk of Malignancy Index compared to IOTA Simple Rules

Background Mathematical predictive models for ovarian tumors have an advantage over subjective assessment due to their relative simplicity, and therefore usefulness for less experienced sonographers. It is currently unclear which predictive model is best at predicting the nature of an ovarian tumor. Purpose To compare the diagnostic predictive accuracy of the International Ovarian Tumour Analysis Simple Rules (IOTA SR) with Risk of Malignancy Index (RMI), to differentiate between benign and malignant ovarian tumors. Material and Methods A total of 202 women diagnosed with ovarian tumor(s) were included. Preoperatively, patients were examined through transvaginal ultrasonography and CA-125 (U/mL) levels were measured. RMI and IOTA SR were determined, and where possible compared to definitive histopathological diagnosis. Results Of the 202 women with ovarian tumors, 168 women were included in this cohort study. Of these tumors, 118 (70.2%) were benign, 17 (10.1%) were borderline, and 33 (19.7%) were malignant. The sensitivity, specificity, and area under the curve for the RMI were 72.0%, 90.7%, and 0.896, respectively. For the IOTA SR, these were 90.0%, 68.6%, and 0.793, respectively. Conclusion This cohort study shows that the RMI is a relatively useful diagnostic model in characterizing ovarian tumors, compared to the IOTA SR. However, due to the relatively low sensitivity of the RMI and high rate of inconclusive results of the IOTA SR, both diagnostic tests do not seem discriminative enough. Therefore, alternative diagnostic models are necessary.

Perfusion-based functional magnetic resonance imaging for differentiating serous borderline ovarian tumors from early serous ovarian cancers in a rat model

Background Differentiation of borderline tumors from early ovarian cancer has recently received increasing attention, since borderline tumors often affect young women of childbearing age who desire to preserve fertility. However, previous studies have demonstrated that non-enhanced magnetic resonance imaging (MRI) sequences cannot sufficiently differentiate these tumors. Purpose To investigate the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating serous borderline ovarian tumors (SBOT) from early serous ovarian cancers (eSOCA). Material and Methods Twenty SBOT and 20 eSOCA rat models were performed with DCE-MRI and IVIM-DWI at 3.0-T MR scanner. Qualitative and quantitative parameters of DCE-MRI were acquired and compared between two groups and correlated with the microvessel density (MVD). The receiver operating characteristic (ROC) curve analyses were conducted to determine their differentiating performances. Results SBOTs presented significantly lower values of the initial area under the enhancement curve (iAUC), volume transfer constant (Ktrans), and extracellular extravascular volume fraction (ve) ( P <  0.05) and a significantly higher value of true diffusion (D) ( P =  0.001) compared with eSOCAs. The diagnostic effectiveness of ve combined with D was significantly better than that of ve or Ktrans alone ( P ≤  0.039). Conclusion DCE-MRI may represent a promising tool for differentiating SBOTs from eSOCAs and may not be replaced by IVIM-DWI. Combining DCE-MRI with DWI may improve the diagnostic performance of ovarian tumors.

Ultrasound-stimulated microbubbles enhances radiosensitivity of ovarian cancer

Background Radiation therapy is regarded as an effective treatment for early ovarian cancer (OC). However, due to radiation resistance caused by DNA double-strand breaks (DSBs) and angiogenesis, the efficacy of radiotherapy for advanced OC is limited and controversial. Purpose To explore whether ultrasound-stimulated microbubbles (USMBs) can enhance the radiosensitivity of OC. Material and Methods OC cells (ES-2) were respectively irradiated with 5-Gy and 10-Gy radiation doses with or without exposure to USMB. Methyl thiazolyltetrazolium (MTT) and colony-formation assays were conducted to detect the viability and proliferation of ES-2 cells after USMBs and ionizing radiation (IR) treatment. Immunofluorescence assays were conducted to examine levels of gamma-H2A histone family member X (γ-H2AX), an indicator for DSBs. Flow cytometry analyses were carried out to assess the apoptosis of ES-2 cells. The angiogenic activity of human umbilical vein endothelial cells (HUVECs) was measured by tube formation assays. Results USMBs enhanced IR-induced suppressive effect on the viability and proliferation of OC cells. The protein levels of phosphorylated γ-H2AX and CHK1 were significantly upregulated after IR treatment and further enhanced by USMBs. In addition, USMBs enhanced the promotion of IR-mediated OC cell apoptosis. The inhibitory effect of IR on angiogenesis was further enhanced by USMBs, and protein levels of AT1R, VEGFA, and EGFR were downregulated by IR in a dose-dependent way and then enhanced by USMB treatment in HUVECs. Conclusions USMB exposure significantly enhances the radiosensitivity of OC by suppressing cell proliferation, promoting OC cell apoptosis, and inhibiting angiogenesis.

Visualizing the autonomic and somatic innervation of the female pelvis with 3D MR neurography: a feasibility study

Background Treatment of female pelvic malignancies often causes pelvic nerve damage. Magnetic resonance (MR) neurography mapping the female pelvic innervation could aid in treatment planning. Purpose To depict female autonomic and somatic pelvic innervation using a modified 3D NerveVIEW sequence. Material and Methods Prospective study in 20 female volunteers (n = 6 normal, n = 14 cervical pathology) who underwent a modified 3D short TI inversion recovery (STIR) turbo spin-echo (TSE) scan with a motion-sensitive driven equilibrium (MSDE) preparation radiofrequency pulse and flow compensation. Modifications included offset independent trapezoid (OIT) pulses for inversion and MSDE refocusing. Maximum intensity projections (MIP) were evaluated by two observers (Observer 1, Observer 2); image quality was scored as 2 = high, 1 = medium, or 0 = low with the sciatic nerve serving as a reference. Conspicuity of autonomic superior (SHP) and bilateral inferior hypogastric plexuses (IHP), hypogastric nerves, and somatic pelvic nerves (sciatic, pudendal) was scored as 2 = well-defined, 1 = poorly defined, or 0 = not seen, and inter-observer agreement was determined. Results Images were of medium to high quality according to both observers agreeing in 15/20 (75%) of individuals. SHP and bilateral hypogastric nerves were seen in 30/60 (50%) of cases by both observers. Bilateral IHP was seen in 85% (34/40) by Observer 1 and in 75% (30/40) by Observer 2. Sciatic nerves were well identified in all cases, while pudendal nerves were seen bilaterally by Observer 1 in 65% (26/40) and by Observer 2 in 72.5% (29/40). Agreement between observers for scoring nerve conspicuity was in the range of 60%–100%. Conclusion Modified 3D NerveVIEW renders high-quality images of the female autonomic and pudendal nerves.