Jinwei Qiang

Multiparametric MRI-based radiomics nomogram for identifying cervix-corpus junction cervical adenocarcinoma from endometrioid adenocarcinoma

To developed a magnetic resonance imaging (MRI) radiomics nomogram to identify adenocarcinoma at the cervix-corpus junction originating from the endometrium or cervix in order to better guide clinical treatment. Between February 2011 and September 2021, the clinicopathological data and MRI in 143 patients with histopathologically confirmed cervical adenocarcinoma (CAC, n = 86) and endometrioid adenocarcinoma (EAC, n = 57) were retrospectively analyzed at the cervix-corpus junction. Radiomics features were extracted from fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and delayed phase contrast-enhanced T1-weighted imaging (CE-T1WI) sequences. A radiomics nomogram was developed integrating radscore with independent clinical risk factors. The area under the curve (AUC) was used to evaluate the diagnostic efficacy of the radscore, nomogram and two different experienced radiologists in differentiating CAC from EAC at the cervix-corpus junction, and Delong test was applied to compare the differences of their diagnostic performance. In the training cohort, the AUC was 0.93 for radscore; 0.97 for radiomics nomograms; 0.85 and 0.86 for radiologists 1 and 2, respectively. Delong test showed that the differential efficacy of nomogram was significant better than those of radiologists in the training cohort (both P < 0.05). The nomogram based on radscore and clinical risk factors could better differentiate CAC from EAC at the cervix-corpus junction than radiologists, and preoperatively and non-invasively identify the origin of adenocarcinoma at the cervix-corpus junction, which facilitates clinicians to make individualized treatment decision.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study

This study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared to conventional radiomics and deep learning models. A retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). A total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P < 0.05). Compared to the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. MRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

Whole‐tumor apparent diffusion coefficient histogram analysis for preoperative risk stratification in endometrial endometrioid adenocarcinoma

AbstractObjectiveTo investigate the application of whole‐tumor apparent diffusion coefficient (ADC) histogram metrics for preoperative risk stratification in endometrial endometrioid adenocarcinoma (EEA).MethodsPreoperative MRI of 502 EEA patients were retrospectively analyzed. Whole tumor ADC histogram analysis was performed with regions of interest drawn on all tumor slices of diffusion‐weighted imaging scans. Risk stratification was based on ESMO‐ESTRO‐ESP guidelines: low‐, intermediate‐, high‐intermediate‐, and high‐risk. Univariable analysis was used to compare ADC histogram metrics (tumor volume, minADC, maxADC, and meanADC; 10th, 25th, 50th, 75th, and 90th percentiles of ADC [recorded as P10, P25, P50, P75, and P90 ADC, respectively]; skewness; and kurtosis) between different risk EEAs, and multivariable logistic regression analysis to determine the optimal metric or combined model for risk stratifications. Receiver operating characteristic curve analysis with the area under the curve (AUC) was used for diagnostic performance evaluation.ResultsA decreasing tendency in multiple ADC values was observed from the low‐ to high‐intermediate‐risk EEAs. The (low + intermediate)‐risk EEAs and low‐risk EEAs had significantly smaller tumor volumes and higher minADCs, meanADCs, P10, P25, P50, P75, and P90 ADCs than the (high‐intermediate + high)‐risk EEAs and non‐low‐risk EEAs (all P &lt; 0.05), respectively. The combined models of the (meanADC + volume) and the (P75 ADC + volume) yielded the largest AUCs of 0.775 and 0.780 in identifying the (low + intermediate)‐ and the low‐risk EEAs from the other EEAs, respectively.ConclusionWhole‐tumor ADC histogram metrics might be helpful for preoperatively identifying low‐ and (low + intermediate)‐risk EEAs, facilitating personalized therapeutic planning.

Multisequence MRI-based radiomics model for predicting POLE mutation status in patients with endometrial cancer

Objectives: Preoperative identification of POLE mutation status would help tailor the surgical procedure and adjuvant treatment strategy. This study aimed to explore the feasibility of developing a radiomics model to pre-operatively predict the pathogenic POLE mutation status in patients with EC. Methods: The retrospective study involved 138 patients with histopathologically confirmed EC (35 POLE-mutant vs 103 non-POLE-mutant). After selecting relevant features with a series of steps, three radiomics signatures were built based on axial fat-saturation T2WI, DWI, and CE-T1WI images, respectively. Then, two radiomics models which integrated features from T2WI + DWI and T2WI + DWI+CE-T1WI were further developed using multivariate logistic regression. The performance of the radiomics model was evaluated from discrimination, calibration, and clinical utility aspects. Results: Among all the models, radiomics model2 (RM2), which integrated features from all three sequences, showed the best performance, with AUCs of 0.885 (95%CI: 0.828–0.942) and 0.810 (95%CI: 0.653–0.967) in the training and validation cohorts, respectively. The net reclassification index (NRI) and integrated discrimination improvement (IDI) analyses indicated that RM2 had improvement in predicting POLE mutation status when compared with the single-sequence-based signatures and the radiomics model1 (RM1). The calibration curve, decision curve analysis, and clinical impact curve suggested favourable calibration and clinical utility of RM2. Conclusions: The RM2, fusing features from three sequences, could be a potential tool for the non-invasive preoperative identification of patients with POLE-mutant EC, which is helpful for developing individualized therapeutic strategies. Advances in knowledge: This study developed a potential surrogate of POLE sequencing, which is cost-efficient and non-invasive.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma

To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). Clinical features, solid component ADC (ADC A significantly higher ADC Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

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

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

The role of volumetric ADC histogram analysis in preoperatively evaluating the tumour subtype and grade of endometrial cancer

To assess the value of volumetric ADC histogram metrics in evaluating the histological subtype and grade of endometrial cancer. Preoperative MRI datasets of 317 patients with endometrial cancer were used to obtain volumetric ADC histogram metrics (tumour volume; minADC, maxADC and meanADC; 10th, 25th, 50th, 75th and 90th percentiles of ADC; skewness; and kurtosis). The Mann-Whitney test or Student's t-test was used to compare the difference in ADC histogram metrics between endometrioid adenocarcinomas (EACs) and serous endometrial cancers (SECs) and between different tumour grades (G1, G2, G3). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the performance of ADC histogram metrics or combined models in predicting the tumour subtype and grade. SECs showed a significantly larger tumour volume (P  0.05). A tumour volume ≥ 7.752 cm Volumetric ADC histogram analysis is helpful for non-invasive preoperatively predicting the subtype of endometrial cancer and differentiating G1 from G2 and G3 EACs.

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

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

Peritumoral MRI Radiomics Features Increase the Evaluation Efficiency for Response to Chemotherapy in Patients With Epithelial Ovarian Cancer

BackgroundIt remains unclear whether extracting peritumoral volume (PTV) radiomics features are useful tools for evaluating response to chemotherapy of epithelial ovarian cancer (EOC).PurposeTo evaluate MRI radiomics signatures (RS) capturing subtle changes of PTV and their added evaluation performance to whole tumor volume (WTV) for response to chemotherapy in patients with EOC.Study TypeRetrospective.Population219 patients aged from 15 to 79 years were enrolled.Field Strength/Sequence3.0 or 1.5T, axial fat‐suppressed T2‐weighted imaging (FS‐T2WI), diffusion‐weighted imaging (DWI), and contrast enhanced T1‐weighted imaging (CE‐T1WI).AssessmentMRI features were extracted from the four axial sequences and six different volumes of interest (VOIs) (WTV and WTV + PTV (WPTV)) with different peritumor sizes (PS) ranging from 1 to 5 mm. Those features underwent preprocessing, and the most informative features were selected using minimum redundancy maximum relevance and least absolute shrinkage and selection operator to construct the RS. The optimal RS, with the highest area under the curve (AUC) of receiver operating characteristic was then integrated with independent clinical characteristics through multivariable logistic regression to construct the radiomics‐clinical model (RCM).Statistical TestsMann–Whitney U test, chi‐squared test, DeLong test, log‐rank test. P &lt; 0.05 indicated a significant difference.ResultsAll the RSs constructed on WPTV exhibited higher AUCs (0.720–0.756) than WTV (0.671). Of which, RS with PS = 2 mm displayed a significantly better performance (AUC = 0.756). International Federation of Gynecology and Obstetrics (FIGO) stage was identified as the exclusive independent clinical evaluation characteristic, and the RCM demonstrated higher AUC (0.790) than the RS, but without statistical significance (P = 0.261).Data ConclusionThe radiomics features extracted from PTV could increase the efficiency of WTV radiomics for evaluating the chemotherapy response of EOC. The cut‐off of 2 mm PTV was a reasonable value to obtain effective evaluation efficiency.Level of Evidence4Technical EfficacyStage 2

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

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

MRI‐Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study

BackgroundPreoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results.PurposeTo develop and validate an objective MRI‐based machine‐learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation.Study TypeRetrospective study of eight clinical centers.PopulationIn all, 501 women with histopathologically‐confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159).Field Strength/SequencePreoperative MRI within 2 weeks of surgery. Single‐ and multiparameter (MP) machine‐learning assessment models were built utilizing the following four MRI sequences: T2‐weighted imaging (T2WI), fat saturation (FS), diffusion‐weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast‐enhanced (CE)‐T1WI.AssessmentDiagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early‐stage MEOT was made. Six radiologists of varying experience also interpreted the MR images.Statistical TestsMann–Whitney U‐test: significance of the clinical characteristics; chi‐square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).ResultsThe MP‐ST model performed better than the MP‐WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early‐stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679–0.924) and external (mean AUC = 0.797; range, 0.744–0.867) validation cohorts.Data ConclusionPerformance of the MRI‐based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women.Level of EvidenceLevel 4.Technical EfficacyStage 2. J. Magn. Reson. Imaging 2020;52:897–904.

Histogram Analysis Comparison of Monoexponential, Advanced Diffusion‐Weighted Imaging, and Dynamic Contrast‐Enhanced MRI for Differentiating Borderline From Malignant Epithelial Ovarian Tumors

BackgroundThe accurate preoperative differentiation between borderline and malignant epithelial ovarian tumors (BEOTs vs. MEOTs) is crucial for determining the proper surgical strategy and improving the patient's postoperative quality of life. Several diffusion and perfusion MRI technologies are valuable for the differentiation; however, which is the best remains unclear.PurposeTo compare the whole solid‐tumor volume histogram analysis of diffusion‐weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and dynamic contrast‐enhanced MRI (DCE‐MRI) in the differentiation of BEOTs vs. MEOTs and to identify the correlations between the perfusion parameters from IVIM and DCE‐MRI.Study TypeRetrospective.PopulationTwenty patients with BEOTs and 42 patients with MEOTs.Field Strength/Sequence1.5T/DWI, DKI, and IVIM models fitting from 13 different b factors and 40 phases DCE‐MRI.AssessmentHistogram metrics were derived from the apparent diffusion coefficient (ADC), diffusion kurtosis (K), diffusion coefficient (Dk), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), volume transfer constant (Ktrans), rate constant (kep), and extravascular extracellular volume fraction (ve).Statistical TestsThe Mann–Whitney U‐test and receiver operating characteristic curve were used to determine the best histogram metrics and parameters. Multivariate logistic regression analysis was used to determine the best combined model for each two from the four technologies. Spearman's rank correlation was used to analyze the correlations between the IVIM and DCE‐MRI parameters.ResultsADC, D, Dk, and D* were significantly higher in BEOTs than in MEOTs (P &lt; 0.05). K, Ktrans, kep, and ve were significantly lower in BEOTs than in MEOTs (P &lt; 0.05). The 10th percentile of Dk was the most reliable single metric, with an area under the curve (AUC) of 0.921. Dk combined with Ktrans yielded the highest AUC of 0.950. A weak inverse correlation was found between D and Ktrans (r = −0.320, P = 0.025) and between D and kep (r = −0.267, P = 0.037).Data ConclusionThe 10th percentile of Dk was the most valuable metric and Dk combined with Ktrans had the best performance for differentiating BEOTs from MEOTs. There was no evident link between perfusion‐related parameters derived from IVIM and DCE‐MRI.Level of Evidence: 4Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020;52:257–268.

MRI‐Based Multiple Instance Convolutional Neural Network for Increased Accuracy in the Differentiation of Borderline and Malignant Epithelial Ovarian Tumors

BackgroundPreoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT vs. MEOT) is challenging and can significantly impact surgical management.PurposeTo develop a multiple instance convolutional neural network (MICNN) that can differentiate BEOT from MEOT, and to compare its diagnostic performance with that of radiologists.Study TypeRetrospective study of eight clinical centers.SubjectsBetween January 2010 and June 2018, a total of 501 women (mean age, 48.93 ± 14.05 years) with histopathologically confirmed BEOT (N = 165) or MEOT (N = 336) were divided into the training (N = 342) and validation cohorts (N = 159).Field Strength/SequenceThree axial sequences from 1.5 or 3 T scanner were used: fast spin echo T2‐weighted imaging with fat saturation (T2WI FS), echo planar diffusion‐weighted imaging, and 2D volumetric interpolated breath‐hold examination of contrast‐enhanced T1‐weighted imaging (CE‐T1WI) with FS.AssessmentThree monoparametric MICNN models were built based on T2WI FS, apparent diffusion coefficient map, and CE‐T1WI. Based on these monoparametric models, we constructed an early multiparametric (EMP) model and a late multiparametric (LMP) model using early and late information fusion methods, respectively. The diagnostic performance of the models was evaluated using the receiver operating characteristic (ROC) curve and compared to the performance of six radiologists with varying levels of experience.Statistical TestsWe used DeLong test, chi‐square test, Mann–Whitney U‐test, and t‐test, with significance level of 0.05.ResultsBoth EMP and LMP models differentiated BEOT from MEOT, with an area under the ROC curve (AUC) of 0.855 (95% CI, 0.795–0.915) and 0.884 (95% CI, 0.831–0.938), respectively. The AUC of the LMP model was significantly higher than the radiologists' pooled AUC (0.884 vs. 0.797).Data ConclusionThe developed MICNN models can effectively differentiate BEOT from MEOT and the diagnostic performances (AUCs) were more superior than that of the radiologists' assessments.Level of Evidence3Technical Efficacy Stage2

Diffusion‐Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High‐Grade Serous Ovarian Carcinoma

BackgroundPreoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high‐grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded.PurposeTo investigate the value of diffusion‐weighted MRI (DW‐MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients.Study TypeProspective.SubjectsA total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years).Fields Strength/SequenceA 3.0 T, readout‐segmented echo‐planar DWI.AssessmentThe MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI‐PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI‐PCI alone; and a fusion model combining the clinical‐morphological information and DWI‐PCI.Statistical TestsMultivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P &lt; 0.05 was considered to be statistically significant.ResultsSixty‐seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI‐PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA‐125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI‐PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI.Data ConclusionsThe combination of two clinical factors, MRI morphological characteristics and DWI‐PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS.Evidence Level2Technical EfficacyStage 2

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.

In vivo detection of dysregulated choline metabolism in paclitaxel-resistant ovarian cancers with proton magnetic resonance spectroscopy

Abstract Background Chemoresistance gradually develops during treatment of epithelial ovarian cancer (EOC). Metabolic alterations, especially in vivo easily detectable metabolites in paclitaxel (PTX)-resistant EOC remain unclear. Methods Xenograft models of the PTX-sensitive and PTX-resistant EOCs were built. Using a combination of in vivo proton-magnetic resonance spectroscopy (1H-MRS), metabolomics and proteomics, we investigated the in vivo metabolites and dysregulated metabolic pathways in the PTX-resistant EOC. Furthermore, we analyzed the RNA expression to validate the key enzymes in the dysregulated metabolic pathway. Results On in vivo 1H-MRS, the ratio of (glycerophosphocholine + phosphocholine) to (creatine + phosphocreatine) ((GPC + PC) to (Cr + PCr))(i.e. Cho/Cr) in the PTX-resistant tumors (1.64 [0.69, 4.18]) was significantly higher than that in the PTX-sensitive tumors (0.33 [0.10, 1.13]) (P = 0.04). Forty-five ex vivo metabolites were identified to be significantly different between the PTX-sensitive and PTX-resistant tumors, with the majority involved of lipids and lipid-like molecules. Spearman’s correlation coefficient analysis indicated in vivo and ex vivo metabolic characteristics were highly consistent, exhibiting the highest positive correlation between in vivo GPC + PC and ex vivo GPC (r = 0.885, P &lt; 0.001). These metabolic data suggested that abnormal choline concentrations were the results from the dysregulated glycerophospholipid metabolism, especially choline metabolism. The proteomics data indicated that the expressions of key enzymes glycerophosphocholine phosphodiesterase 1 (GPCPD1) and glycerophosphodiester phosphodiesterase 1 (GDE1) were significantly lower in the PTX-resistant tumors compared to the PTX-sensitive tumors (both P &lt; 0.01). Decreased expressions of GPCPD1 and GDE1 in choline metabolism led to an increased GPC levels in the PTX-resistant EOCs, which was observed as an elevated total choline (tCho) on in vivo 1H-MRS. Conclusions These findings suggested that dysregulated choline metabolism was associated with PTX-resistance in EOCs and the elevated tCho on in vivo 1H-MRS could be as an indicator for the PTX-resistance in EOCs.

Multiparametric MRI‐Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early‐Stage Cervical Cancer

BackgroundLymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early‐stage cervical cancer.PurposeTo establish a multiparametric MRI (mpMRI)‐based radiomics nomogram for preoperatively predicting LNM status.Study TypeRetrospective.PopulationAmong 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort.Field StrengthRadiomic features were extracted from a 1.5T mpMRI scan (T1‐weighted imaging [T1WI], fat‐saturated T2‐weighted imaging [FS‐T2WI], contrast‐enhanced [CE], diffusion‐weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps).AssessmentThe performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated.Statistical TestsThe least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t‐test and chi‐squared test were used to compare the differences in continuous and categorical variables, respectively.ResultsThe radiomic signature allowed a good discrimination between the LNM and non‐LNM groups, with a C‐index of 0.856 (95% confidence interval [CI], 0.794–0.918) in the primary cohort and 0.883 (95% CI, 0.809–0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C‐index, 0.882 [95% CI, 0.827–0.937], C‐index, 0.893 [95% CI, 0.822–0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.Data ConclusionA radiomics nomogram was developed by incorporating the radiomics signature with the MRI‐reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early‐stage cervical cancer.Level of Evidence3Technical Efficacy Stage2 J. Magn. Reson. Imaging 2020;52:885–896.

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

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

Deep Learning Nomogram for the Identification of Deep Stromal Invasion in Patients With Early‐Stage Cervical Adenocarcinoma and Adenosquamous Carcinoma: A Multicenter Study

BackgroundDeep stromal invasion (DSI) is one of the predominant risk factors that determined the types of radical hysterectomy (RH). Thus, the accurate assessment of DSI in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC) can facilitate optimal therapy decision.PurposeTo develop a nomogram to identify DSI in cervical AC/ASC.Study TypeRetrospective.PopulationSix hundred and fifty patients (mean age of 48.2 years) were collected from center 1 (primary cohort, 536), centers 2 and 3 (external validation cohorts 1 and 2, 62 and 52).Field Strength/Sequence5‐T, T2‐weighted imaging (T2WI, SE/FSE), diffusion‐weighted imaging (DWI, EPI), and contrast‐enhanced T1‐weighted imaging (CE‐T1WI, VIBE/LAVA).AssessmentThe DSI was defined as the outer 1/3 stromal invasion on pathology. The region of interest (ROI) contained the tumor and 3 mm peritumoral area. The ROIs of T2WI, DWI, and CE‐T1WI were separately imported into Resnet18 to calculate the DL scores (TDS, DDS, and CDS). The clinical characteristics were retrieved from medical records or MRI data assessment. The clinical model and nomogram were constructed by integrating clinical independent risk factors only and further combining DL scores based on primary cohort and were validated in two external validation cohorts.Statistical TestsStudent's t‐test, Mann–Whitney U test, or Chi‐squared test were used to compare differences in continuous or categorical variables between DSI‐positive and DSI‐negative groups. DeLong test was used to compare AU‐ROC values of DL scores, clinical model, and nomogram.ResultsThe nomogram integrating menopause, disruption of cervical stromal ring (DCSRMR), DDS, and TDS achieved AU‐ROCs of 0.933, 0.807, and 0.817 in evaluating DSI in primary and external validation cohorts. The nomogram had superior diagnostic ability to clinical model and DL scores in primary cohort (all P &lt; 0.0125 [0.05/4]) and CDS (P = 0.009) in external validation cohort 2.Data ConclusionThe nomogram achieved good performance for evaluating DSI in cervical AC/ASC.Level of Evidence3Technical EfficacyStage 2