Application of computer-aided diagnosis to predict malignancy in BI-RADS 3 breast lesions

Abstract

PurposeTo evaluate the ability of computer-aided diagnosis (CAD) system (S-Detect) to identify malignancy in ultrasound (US) -detected BI-RADS 3 breast lesions. Materials and methods148 patients with 148 breast lesions categorized as BI-RADS 3 were included in the study between January 2021 and September 2022. The malignancy rate, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated. ResultsIn this study, 143 breast lesions were found to be benign, and 5 breast lesions were malignant (malignancy rate, 3.4 %, 95 % confidence interval (CI): 0.5–6.3). The malignancy rate rose significantly to 18.2 % (4/22, 95 % CI: 2.1–34.3) in the high-risk group with a “possibly malignant” CAD result (p = 0.017). With a “possibly benign” CAD result, the malignancy rate decreased to 0.8 % (1/126, 95 % CI: 0–2.2) in the low-risk group (p = 0.297). The AUC, sensitivity, specificity, accuracy, PPV, and NPV of the CAD system in BI-RADS 3 breast lesions were 0.837 (95 % CI: 77.7–89.6), 80.0 % (95 % CI: 73.6–86.4), 87.4 % (95 % CI: 82.0–92.7), 87.2 % (95 % CI: 81.8–92.6), 18.2 % (95 % CI: 2.1–34.3) and 99.2 % (95 % CI: 97.8–100.0), respectively. ConclusionsCAD system (S-Detect) enables radiologists to distinguish a high-risk group and a low-risk group among US-detected BI-RADS 3 breast lesions, so that patients in the low-risk group can receive follow-up without anxiety, while those in the high-risk group with a significantly increased malignancy rate should actively receive biopsy to avoid delayed diagnosis of breast cancer.