Performance of Radiologist in Interpretation of Non-mass Lesions Detected by Automated Breast Ultrasound: With and Without Commercially Available AI.
Authors
Affiliations (2)
Affiliations (2)
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
- Department of Ultrasound, Xijing Hospital, Fourth Military Medical University, Xi'an, China. Electronic address: [email protected].
Abstract
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). 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. 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). Our study shows that readers can improve their diagnosis of NMLs after using AI-CAD systems, especially for novice readers.