Performance of Radiologist in Interpretation of Non-mass Lesions Detected by Automated Breast Ultrasound: With and Without Commercially Available AI.

OBJECTIVES: To evaluate the performance of radiologists with artificial intelligence (AI)-based computer-aided detection (CAD) systems on automated breast ultrasound (ABUS) for breast non-mass lesions (NMLs). METHODS: From July 2020 to April 2022, patients who underwent ABUS examinations and described NMLs were included in this retrospective study. First, we compared the performance of two AI-CAD systems for diagnosing NMLs. Then, the superior-performing was selected with 4 radiologists with different levels of experience to assess the ability to diagnose NMLs with and without AI-CAD systems. RESULTS: A total of 251 patients with 251 NMLs were enrolled, of which 54.2% (136/251) were benign and 45.8% (115/251) were malignant. Comparing BU-CAD and QV-CAD, the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 45.2% versus 64.3% (p < 0.001), 77.2% versus 64.7% (p = 0.005), and 0.63 versus 0.68 (p = 0.17), respectively. Given the clinical characteristic of NMLs having a high risk of missed diagnosis, we prioritized sensitivity over specificity in our considerations. Consequently, we selected QV-CAD for its high sensitivity and numerically superior AUC. With CAD support, the mean AUC was improved from 0.78 to 0.83 (p = 0.04) for all readers; for novice readers, the mean AUC was improved from 0.73 to 0.80 (p < 0.001); for experienced readers, there were no differences in AUC among the two reading modes 0.83 versus 0.85 (p = 0.14). CONCLUSIONS: Our study shows that readers can improve their diagnosis of NMLs after using AI-CAD systems, especially for novice readers.