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

Abstract

Objectives: To 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 methods: This 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.

Results: In 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.

Conclusions: The 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.