AUTHOR=Liu Lu , Cai Wenjun , Tian Hongyan , Wu Beibei , Zhang Jing , Wang Ting , Hao Yi , Yue Guanghui 

TITLE=Ultrasound image-based nomogram combining clinical, radiomics, and deep transfer learning features for automatic classification of ovarian masses according to O-RADS

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1377489

DOI=10.3389/fonc.2024.1377489

ISSN=2234-943X

ABSTRACT=Accurate and rapid discrimination between benign and malignant ovarian masses is crucial for optimal patient management. This study aimed to establish an ultrasound images-based nomogram combining clinical, radiomics, and deep transfer learning features to automatically classify the ovarian masses into low risk and intermediate-high risk of malignancy lesions according to the Ovarian-Adnexal Reporting and Data System (O-RADS). The ultrasound images of 1080 patients with 1080 ovarian masses were included. The training cohort consisting of 683 patients was collected at the South China Hospital of Shenzhen University and the test cohort consisting of 397 patients was collected at the Shenzhen University General Hospital. The workflow included image segmentation, feature extraction, feature selection, and model construction. The pre-trained Resnet-101 model achieved the best performance. Among the different monomodal features and fusion features models, nomogram achieved the highest level of diagnostic performance (AUC: 0.930, accuracy: 84.9%, sensitivity: 93.5%, specificity: 81.7%, PPV: 65.4%, NPV: 97.1%, precision: 65.4%). The diagnostic indexes of the nomogram were higher than that of junior radiologists, and the diagnostic indexes of junior radiologists significantly improved with the assistance of the model. The calibration curves showed good agreement between the prediction of nomogram and actual classification of ovarian masses. The decision curve analysis showed the nomogram was clinically useful. This model exhibited a satisfactory diagnostic performance compared to junior radiologists. It has the potential to improve the level of expertise of junior radiologists and provide a fast and effective method for ovarian cancer screening.