Adnexal Lesion Discrimination Using Deep Learning Analysis of Dynamic Contrast-enhanced US Images

Purpose To develop a multimodality deep learning model (Ovarian Cancer Network [OCNet]) using dynamic contrast-enhanced US images to classify adnexal lesions. Materials and Methods This retrospective study included patients with pathologically confirmed adnexal lesions detected at US across 14 hospitals in China between January 2018 and July 2023. Data were divided into the training set (n = 275), internal testing set (n = 57), and external testing set (n = 63). Two deep learning models (OCNet_manual and OCNet_automated) were developed and compared with Ovarian-Adnexal Reporting and Data System (O-RADS) US and the Assessment of Different Neoplasias in the Adnexa (ADNEX) model. Diagnostic performances of radiologists with and without assistance of OCNet were also assessed. Results A total of 395 female patients (median age, 43 years; IQR, 31-55 years) were included (252 benign and 143 malignant lesions). OCNet_manual and OCNet_automated achieved an area under the receiver operating characteristic curve (AUC) of 0.94 (95% CI: 0.89, >0.99) and 0.91 (95% CI: 0.83, 0.99), respectively, outperforming O-RADS US (AUC, 0.79; 95% CI: 0.68, 0.89; P = .002 and P = .03, respectively) and the ADNEX model (AUC, 0.86; 95% CI: 0.77, 0.95; P = .04 and P = .36, respectively). Additionally, the assistance of OCNet enhanced diagnostic performance for junior radiologists, improving the average AUC from 0.86 to 0.94 and the average specificity from 52% to 73%. Conclusion The OCNet model achieved higher performance than O-RADS US and the ADNEX model for classifying adnexal lesions and improved the diagnostic performance of junior radiologists. Keywords: Adnexal Lesion, Deep Learning, Contrast-enhanced US, Multimodal Supplemental material is available for this article. © RSNA 2025 See also commentary by Huber and Adams in this issue.