Work

OTC-NET: A Multimodal Method for Accurate Diagnosis of Ovarian Cancer in O-RADS Category 4 Masses

Year: 2025

Type: article

Abstract: Background: Ovarian cancer is the deadliest female reproductive malignancy. Accurate preoperative differentiation of benign and malignant ovarian masses is critical for appropriate treatment. O-RADS category 4 lesions present a wide range of malignant risk, challenging radiologists. Ultrasonic images are the primary focus of current deep learning models, with no consideration for clinical data. Methods: We proposed OTC-NET, a model that uses multimodal data for classification, which combines ultrasound images and clinical information to improve the classification ability of O-RADS 4 ovarian masses. Results: OTC-NET outperforms seven deep learning models and three radiologists of varying experience, with AUC significantly higher than junior (p < 0.001), intermediate (p < 0.01), and senior (p < 0.05) radiologists. Additionally, OTC-NET–assisted diagnosis notably improves AUC and accuracy of junior and senior radiologists (p < 0.05). Conclusions: These results indicate that OTC-NET provides superior diagnostic accuracy and has strong potential for clinical application.

Source: Cancers

DOI: 10.3390/cancers17213466