ArticleDiagnostics (Basel, Switzerland)2022
Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Subgroup performance of a commercial digital breast tomosynthesis model for breast cancer detection.Nature communications · 2026Article
- Article
- Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence.European journal of radiology open · 2025Article
- Multimodal integration strategies for clinical application in oncology.Frontiers in pharmacology · 2025Review
- Segmentation for mammography classification utilizing deep convolutional neural network.BMC medical imaging · 2024Article
- Aptamer-functionalized MOFs and AI-driven strategies for early cancer diagnosis and therapeutics.Biotechnology letters · 2024Review
- Artificial Intelligence Improves Detection of Supplemental Screening Ultrasound-detected Breast Cancers in Mammography.Journal of breast cancer · 2023Article
- BUViTNet: Breast Ultrasound Detection via Vision Transformers.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical deployment environments: Five pillars of translational machine learning for health.Frontiers in digital health · 2022Review
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4 authors.
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Abstract
To investigate whether artificial-intelligence-based, computer-aided diagnosis (AI-CAD) could facilitate the detection of missed cancer on digital mammography, a total of 204 women diagnosed with breast cancer with diagnostic (present) and prior mammograms between 2018 and 2020 were included in this study. Two breast radiologists reviewed the mammographic features and classified them into true negative, minimal sign or missed cancer. They analyzed the AI-CAD results with an abnormality score and assessed whether the AI-CAD correctly localized the known cancer sites. Of the 204 cases, 137 were classified as true negative, 33 as minimal signs, and 34 as missed cancer. The sensitivity, specificity and diagnostic accuracy of AI-CAD were 84.7%, 91.5% and 86.3% on diagnostic mammogram and 67.2%, 91.2% and 83.38% on prior mammogram, respectively. The AI-CAD correctly localized 27 cases from 34 missed cancers on prior mammograms. The findings in the preceding mammography of AI-CAD-detected missed cancer were common in the order of calcifications, focal asymmetry and asymmetry. Asymmetry was the most common finding among the seven cases, which could not be detected by AI-CAD in the missed cases (5/7). The assistance of AI-CAD can be helpful in the early detection of breast cancer in mammography screenings.
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