Artificial Intelligence for Classification of Soft-Tissue Masses at US

To train convolutional neural network (CNN) models to classify benign and malignant soft-tissue masses at US and to differentiate three commonly observed benign masses. In this retrospective study, US images obtained between May 2010 and June 2019 from 419 patients (mean age, 52 years 18 [standard deviation]; 250 women) with histologic diagnosis confirmed at biopsy or surgical excision (n 227) or masses that demonstrated imaging characteristics of lipoma, benign peripheral nerve sheath tumor, and vascular malformation (n 192) were included. Images in patients with a histologic diagnosis (n 227) were used to train and evaluate a CNN model to distinguish malignant and benign lesions. Twenty percent of cases were withheld as a test dataset, and the remaining cases were used to train the model with a 75%-25% training-validation split and fourfold cross-validation. Performance of the model was compared with retrospective interpretation of the same dataset by two experienced musculoskeletal radiologists, blinded to clinical history.