Ting Chen

Preoperative Assessment of MRI‐Invisible Early‐Stage Endometrial Cancer With MRI‐Based Radiomics Analysis

BackgroundRadiomics‐based analyses have demonstrated impact on studies of endometrial cancer (EC). However, there have been no radiomics studies investigating preoperative assessment of MRI‐invisible EC to date.PurposeTo develop and validate radiomics models based on sagittal T2‐weighted images (T2WI) and T1‐weighted contrast‐enhanced images (T1CE) for the preoperative assessment of MRI‐invisible early‐stage EC and myometrial invasion (MI).Study TypeRetrospective.PopulationOne hundred fifty‐eight consecutive patients (mean age 50.7 years) with MRI‐invisible endometrial lesions were enrolled from June 2016 to March 2022 and randomly divided into the training (n = 110) and validation cohort (n = 48) using a ratio of 7:3.Field Strength/Sequence3‐T, T2WI, and T1CE sequences, turbo spin echo.AssessmentTwo radiologists performed image segmentation and extracted features. Endometrial lesions were histopathologically classified as benign, dysplasia, and EC with or without MI. In the training cohort, 28 and 20 radiomics features were selected to build Model 1 and Model 2, respectively, generating rad‐score 1 (RS1) and rad‐score 2 (RS2) for evaluating MRI‐invisible EC and MI.Statistical TestsThe least absolute shrinkage and selection operator logistic regression method was used to select radiomics features. Mann–Whitney U tests and Chi‐square test were used to analyze continuous and categorical variables. Receiver operating characteristic curve (ROC) and decision curve analysis were used for performance evaluation. The area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated. A P‐value <0.05 was considered statistically significant.ResultsModel 1 had good performance for preoperative detecting of MRI‐invisible early‐stage EC in the training and validation cohorts (AUC: 0.873 and 0.918). In addition, Model 2 had good performance in assessment of MI of MRI‐invisible endometrial lesions in the training and validation cohorts (AUC: 0.854 and 0.834).Data ConclusionMRI‐based radiomics models may provide good performance for detecting MRI‐invisible EC and MI.Evidence Level3Technical EfficacyStage 2

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.