Artificial intelligence-based computer-aided diagnosis for breast cancer detection on digital mammography in Hong Kong.

IF 3.1 4区 医学 Q1 MEDICINE, GENERAL & INTERNAL Hong Kong Medical Journal Pub Date : 2024-12-19 DOI:10.12809/hkmj2310920
S M Yu, C Y M Young, Y H Chan, Y S Chan, C Tsoi, M N Y Choi, T H Chan, J Leung, W C W Chu, E H Y Hung, H H L Chau
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Abstract

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.

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来源期刊
Hong Kong Medical Journal
Hong Kong Medical Journal MEDICINE, GENERAL & INTERNAL-
CiteScore
1.50
自引率
14.80%
发文量
117
审稿时长
10 weeks
期刊介绍: The HKMJ is a Hong Kong-based, peer-reviewed, general medical journal which is circulated to 6000 readers, including all members of the HKMA and Fellows of the HKAM. The HKMJ publishes original research papers, review articles, medical practice papers, case reports, editorials, commentaries, book reviews, and letters to the Editor. Topics of interest include all subjects that relate to clinical practice and research in all branches of medicine. The HKMJ welcomes manuscripts from authors, but usually solicits reviews. Proposals for review papers can be sent to the Managing Editor directly. Please refer to the contact information of the Editorial Office.
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