Artificial intelligence–based computer-aided diagnosis for breast cancer detection on digital mammography in Hong Kong
SM Yu, Catherine YM Young, YH Chan, YS Chan, Carita Tsoi, M. Choi, TH Chan, Jason Chi Shun Leung, Chiu‐Wing Winnie Chu, Esther H. Y. Hung, Helen H L Chau · Hong Kong Medical Journal · 2024
INTRODUCTION: Research concerning artificial intelligence in breast cancer detection has primarily focused on population screening. However, Hong Kong lacks a population-based screening programme. This study aimed to evaluate the potential of artificial intelligence-based computer-assisted diagnosis (AI-CAD) program in symptomatic clinics in Hong Kong and analyse the impact of radio-pathological breast cancer phenotype on AI-CAD performance. METHODS: In total, 398 consecutive patients with 414 breast cancers were retrospectively identified from a local, prospectively maintained database managed by two tertiary referral centres between January 2020 and September 2022. The full-field digital mammography images were processed using a commercial AI-CAD algorithm. An abnormality score <30 was considered a false negative, whereas a score of ≥90 indicated a high-score tumour. Abnormality scores were analysed with respect to the clinical and radio-pathological characteristics of breast cancer, tumour-to-breast area ratio (TBAR), and tumour distance from the chest wall for cancers presenting as a mass. RESULTS: The median abnormality score across the 414 breast cancers was 95.6; sensitivity was 91.5% and specificity was 96.3%. High-score cancers were more often palpable, invasive, and presented as masses or architectural distortion (P<0.001). False-negative cancers were smaller, more common in dense breast tissue, and presented as asymmetrical densities (P<0.001). Large tumours with extreme TBARs and locations near the chest wall were associated with lower abnormality scores (P<0.001). Several strengths and limitations of AI-CAD were observed and discussed in detail. CONCLUSION: Artificial intelligence-based computer-assisted diagnosis shows potential value as a tool for breast cancer detection in symptomatic setting, which could provide substantial benefits to patients.