A Deep Learning Model System for Diagnosis and Management of Adnexal Masses

Simple Summary This was a multicenter study on the development of a deep learning (DL) model system to diagnose adnexal masses on ultrasound images. There were three innovation points. First, the DL system contained five models: a detector, a mass segmentor, a papillary segmentor, a type classifier, and a pathological subtype classifier. Therefore, the system could finish the entire diagnosis process for adnexal masses on ultrasound images. Second, the DL system could discriminate borderline tumors from benign and malignant tumors with the assistance of annotations for papillary projections (which is a significant morphological feature of borderline tumors). Third, the benign tumors were classified into five pathological subtypes with different risks of clinical complication and accurate disease. Appropriate clinical management of adnexal masses requires a detailed diagnosis. We retrospectively collected ultrasound images of 1559 cases from the first Center of Chinese PLA General Hospital and developed a fully automatic deep learning (DL) model system to diagnose adnexal masses. The DL system contained five models: a detector, a mass segmentor, a papillary segmentor, a type classifier, and a pathological subtype classifier. To test the DL system, 462 cases from another two hospitals were recruited. The DL system identified benign, borderline, and malignant tumors with macro-F1 scores that varied from 0.684 to 0.791, a benefit to preventing both delayed and overextensive treatment. The macro-F1 scores of the pathological subtype classifier to categorize the benign masses varied from 0.714 to 0.831. The detailed classification can inform clinicians of the corresponding complications of each pathological subtype of benign tumors. The distinguishment between borderline and malignant tumors and inflammation from other subtypes of benign tumors need further study. The accuracy and sensitivity of the DL system were comparable to that of the expert and intermediate sonographers and exceeded that of the junior sonographer.