Distinction between phyllodes tumor and fibroadenoma in breast ultrasound using deep learning image analysis

Abstract

Purpose: To evaluate the accuracy of a deep learning software (DLS) in the discrimination between phyllodes tumors (PT) and fibroadenomas (FA). Methods: In this IRB-approved, retrospective, single-center study, we collected all ultrasound images of histologically secured PT (n = 11, 36 images) and a random control group with FA (n = 15, 50 images). The images were analyzed with a DLS designed for industrial grade image analysis, with 33 images withheld from training for validation purposes. The lesions were also interpreted by four radiologists. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). Sensitivity, specificity, negative and positive predictive values were calculated at the optimal cut-off (Youden Index). Results: The DLS was able to differentiate between PT and FA with good diagnostic accuracy (AUC = 0.73) and high negative predictive value (NPV = 100%). Radiologists showed comparable accuracy (AUC 0.60–0.77) at lower NPV (64–80%). When performing the readout together with the DLS recommendation, the radiologist’s accuracy showed a non-significant tendency to improve (AUC 0.75–0.87, p = 0.07). Conclusion: Deep learning based image analysis may be able to exclude PT with a high negative predictive value. Integration into the clinical workflow may enable radiologists to more confidently exclude PT, thereby reducing the number of unnecessary biopsies. Keywords: Breast imaging, Ultrasound, Phyllodes, Fibroadenoma, Deep learning, Computer assisted diagnosis