Background Preoperative differentiation between benign and borderline epithelial ovarian tumors (EOTs) is challenging and can significantly impact clinical decision making. The purpose was to investigate whether radiomics based on T2-weighted MRI can discriminate between benign and borderline EOTs preoperatively. Methods A total of 417 patients (309, 78, and 30 samples in the training and internal and external validation sets) with pathologically proven benign and borderline EOTs were included in this multicenter study. In total, 1130 radiomics features were extracted from manually delineated tumor volumes of interest on images. The following three different models were constructed and evaluated: radiomics features only (radiomics model); clinical and radiological characteristics only (clinic-radiological model); and a combination of them all (combined model). The diagnostic performances of models were assessed using receiver operating characteristic (ROC) analysis, and area under the ROC curves (AUCs) were compared using the DeLong test. Results The best machine learning algorithm to distinguish borderline from benign EOTs was the logistic regression. The combined model achieved the best performance in discriminating between benign and borderline EOTs, with an AUC of 0.86 ± 0.07. The radiomics model showed a moderate AUC of 0.82 ± 0.07, outperforming the clinic-radiological model (AUC of 0.79 ± 0.06). In the external validation set, the combined model performed significantly better than the clinic-radiological model (AUCs of 0.86 vs. 0.63, p = 0.021 [DeLong test]). Conclusions Radiomics, based on T2-weighted MRI, can provide critical diagnostic information for discriminating between benign and borderline EOTs, thus having the potential to aid personalized treatment options.

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.

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BackgroundThe preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision‐making.PurposeTo investigate the performance of T2‐weighted (T2W) MRI‐based deep learning (DL) and radiomics methods for PM evaluation in EOC patients.Study TypeRetrospective.PopulationFour hundred seventy‐nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]).Field Strength/Sequence1.5 or 3 T/fat‐suppression T2W fast or turbo spin‐echo sequence.AssessmentResNet‐50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision‐level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated.Statistical TestsReceiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two‐tailed P < 0.05 was considered significant.ResultsThe ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789).Data ConclusionsT2W MRI‐based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision‐making.Evidence Level4Technical EfficacyStage 2