ArticleFrontiers in oncology2022
Magnetic resonance imaging radiomics to differentiate ovarian sex cord-stromal tumors and primary epithelial ovarian cancers.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Discrimination of benign and malignant ovarian sex cord-stromal tumors through the analysis of clinical features, MR imaging, and MR-based radiomics.Frontiers in oncology · 2026Article
- Diagnostic value of multiparametric MRI combined with an interpretable machine learning model in the differentiation of benign and malignant ovarian-adnexal lesions classified as O-RADS 4.BMC medical imaging · 2025Article
- Findings on conventional ultrasonography and contrast-enhanced ultrasonography in different histopathological subtypes of ovarian thecoma-fibroma group.BMC medical imaging · 2025Article
- Radiomics and radiogenomics: extracting more information from medical images for the diagnosis and prognostic prediction of ovarian cancer.Military Medical Research · 2024Review
- CT-based radiomic analysis for categorization of ovarian sex cord-stromal tumors and epithelial ovarian cancers.Abdominal radiology (New York) · 2024Article
- Advances in ovarian cancer radiomics: a bibliometric analysis from 2010 to 2024.Frontiers in oncology · 2024Article
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11 authors.
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Abstract
Objective: To evaluate the diagnostic ability of magnetic resonance imaging (MRI) based radiomics and traditional characteristics to differentiate between Ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs). Methods: We consecutively included a total of 148 patients with 173 tumors (81 SCSTs in 73 patients and 92 EOCs in 75 patients), who were randomly divided into development and testing cohorts at a ratio of 8:2. Radiomics features were extracted from each tumor, 5-fold cross-validation was conducted for the selection of stable features based on development cohort, and we built radiomics model based on these selected features. Univariate and multivariate analyses were used to identify the independent predictors in clinical features and conventional MR parameters for differentiating SCSTs and EOCs. And nomogram was used to visualized the ultimately predictive models. All models were constructed based on the logistic regression (LR) classifier. The performance of each model was evaluated by the receiver operating characteristic (ROC) curve. Calibration and decision curves analysis (DCA) were used to evaluate the performance of models. Results: The final radiomics model was constructed by nine radiomics features, which exhibited superior predictive ability with AUCs of 0.915 (95%CI: 0.869-0.962) and 0.867 (95%CI: 0.732-1.000) in the development and testing cohorts, respectively. The mixed model which combining the radiomics signatures and traditional parameters achieved the best performance, with AUCs of 0.934 (95%CI: 0.892-0.976) and 0.875 (95%CI: 0.743-1.000) in the development and testing cohorts, respectively. Conclusion: We believe that the radiomics approach could be a more objective and accurate way to distinguish between SCSTs and EOCs, and the mixed model developed in our study could provide a comprehensive, effective method for clinicians to develop an appropriate management strategy.
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