Artificial Intelligence for Classification of Soft-Tissue Masses at US

Purpose: 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. Materials and Methods: 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. A second group of US images from 275 of the 419 patients containing the three common benign masses was used to train and evaluate a separate model to differentiate between the masses. The models were trained on the Keras machine learning platform (version 2.3.1), with a modified pretrained VGG16 network. Performance metrics of the model and of the radiologists were compared by using the McNemar test, and 95% CIs for performance metrics were estimated by using the Clopper-Pearson method (accuracy, recall, specificity, and precision) and the DeLong method (area under the receiver operating characteristic curve). Results: The model trained to classify malignant and benign masses demonstrated an accuracy of 79% (95% CI: 68, 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.84, 0.98), matching the performance of two expert readers. Performance of the model distinguishing three benign masses was lower, with an accuracy of 71% (95% CI: 61, 80) on the test data. Conclusion: The trained CNN was capable of differentiating between benign and malignant soft-tissue masses depicted on US images, with performance matching that of two experienced musculoskeletal radiologists. (C) RSNA, 2020

Automated summary

The study aimed to train CNN models to classify benign and malignant soft-tissue masses on US images and differentiate three common benign masses. The trained CNN model demonstrated excellent performance in distinguishing malignant and benign masses, matching that of experienced musculoskeletal radiologists.