The value of a radiomics model in predicting ovarian malignancy: a retrospective multi-center comparison with O-RADS and radiologists.

objectivesTo develop an MRI-based radiomics model for ovarian masses categorization and to compare the model performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and radiologists' assessments. MATERIALS AND

methodsThis retrospective multicenter study included 497 patients (249 benign, 248 malignant) allocated to training, internal, and external validation sets (293/124/80 masses, respectively). Radiomics features were extracted from preoperative MRI. Features were selected using minimum redundancy, maximum relevance, and the least absolute shrinkage and selection operator algorithm. Diagnostic performance of the radiomics model, O-RADS, and independent assessments by junior and senior radiologists was evaluated via the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

resultsIn external validation, the radiomics model (AUC = 0.939) outperformed O-RADS (AUC = 0.862; p = 0.047) and the junior radiologist (AUC = 0.802; p = 0.003) and was similar to the senior radiologist (AUC = 0.886; p = 0.231). Subgroup analysis of O-RADS score 4 showed the model (AUC = 0.879) outperformed both radiologists (junior: p = 0.001; senior: p = 0.005). For solid, cystic-solids, and cystic masses, the AUCs of the model were 0.921, 0.975, and 0.848, respectively.

conclusionsThe performance of the radiomics model to categorize ovarian masses was superior to O-RADS and junior radiologists and similar to senior radiologists. As a complementary tool to O-RADS, it allows for refined risk stratification for ovarian masses with an O-RADS score of 4 and different morphological characteristics, providing clinicians with quantitative decision support to improve preoperative diagnosis and guide treatment planning. CRITICAL RELEVANCE STATEMENT: Radiomics model provides improved risk stratification and supports precise clinical decision-making for ovarian masses with an O-RADS score of 4 and solid, cystic-solid ovarian masses, thereby improving the management of patients with ovarian masses. KEY POINTS: MRI-based radiomics allows for the characterization of ovarian masses with high accuracy. Radiomics helps differentiate between benign and malignant ovarian masses with an O-RADS score of 4. For solid, cystic-solid, and cystic masses, the radiomics model exhibited higher or similar performance to that of the O-RADS and radiologists.