Abstract

Abstract Objectives To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumors (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences. Methods We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists’ diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting six radiologists with different levels of experience. Results Upon testing, Xception model demonstrated the best diagnostic performance (AUC: 0.87, 95%CI: 0.81-0.92), outperforming all radiologists (all p < 0.05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively. Conclusions The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work). ADVANCES IN KNOWLEDGE We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate two types of breast tumors which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.