Development and Multicenter Validation of a Hybrid Radiomics-Deep Learning Model for Risk Stratification of O-RADS 4 Adnexal Masses.
Authors
Affiliations (5)
Affiliations (5)
- Department of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China (Y.S., H.S., Q.Z., L.D., M.H., L.Z., Y.J., L.W., and H.X.).
- Department of Medical Ultrasonics, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou, PR China (Y.G.).
- Department of Medical Ultrasonics, Shenzhen Nanshan Maternity and Child Healthcare Hospital, Shenzhen, PR China (R.S.).
- Department of Medical Ultrasonics, Zhongshan City People's Hospital, Zhongshan, PR China (Y.D.).
- Department of Medical Ultrasonics, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, PR China (Y.S., H.S., Q.Z., L.D., M.H., L.Z., Y.J., L.W., and H.X.). Electronic address: [email protected].
Abstract
To develop and validate an artificial intelligence-driven hybrid model integrating ultrasound radiomics, deep learning (DL) signatures, and clinical predictors for differentiating benign from borderline/malignant Ovarian-Adnexal Reporting and Data System (O-RADS) 4 adnexal masses, and to evaluate its utility as a radiologist decision-support tool. This multicenter study retrospectively enrolled 653 patients from four centers (521 from Center A [training, n = 405; internal validation, n = 116]; 132 from Centers B-D [external validation]), and prospectively enrolled 126 patients from Center A. Pathological results served as the gold standard. Radiomics features were extracted to generate a Rad-score using machine learning classifiers. A transfer learning-based DL model using convolutional neural networks produced a DL-score. A hybrid model integrating Rad-score, DL-score, and clinical predictors was constructed. Diagnostic performance of models was assessed using the area under the receiver operating characteristic curve (AUC). In the prospective validation set, six radiologists interpreted cases with and without hybrid model assistance. Diagnostic differences were assessed using McNemar's χ² test, and inter-reader agreement by Cohen's kappa. The hybrid model demonstrated good performance with AUCs of 0.88 (95% confidence interval [CI]: 0.82-0.93) and 0.86 (95% CI: 0.78-0.92) in the prospective and external validation sets. Model assistance significantly improved accuracy and specificity across all readers (p < 0.05), with inter-reader agreement improving from kappa 0.49 to 0.73. The model stratified lesions into low-risk (4a) and high-risk (4b) subcategories with consistently distinct malignancy rates across all datasets (4a: 4.5%-16.1%; 4b: 75.4%-97.1%). The hybrid model demonstrated good performance for risk stratification of O-RADS 4 adnexal masses and improved radiologist performance across experience levels.