Hong Kong medical journal = Xianggang yi xue za zhi · 2024
Artificial intelligence-based computer-aided diagnosis for breast cancer detection on digital mammography in Hong Kong.
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Yu S M, Young C Y M, Chan Y H, Chan Y S, Tsoi C, Choi M N Y, Chan T H, Leung J, Chu W C W, Hung E H Y, Chau H H L
Аннотация
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.
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.