ArticleQuantitative imaging in medicine and surgery2026
Mammogram-based AI risk assessment in patients with dense breasts undergoing supplemental molecular breast imaging.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Evaluating the Performance of Mammogram-Based AI Risk Model in Predicting Subsequent Breast Cancer in Women with a Prior History of Breast Cancer.Journal of clinical medicine · 2026Article
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9 authors.
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Abstract
Background: Image-based artificial intelligence (AI) risk models can estimate short-term breast cancer risk directly from mammograms and may outperform traditional questionnaire-based tools. However, risk stratification remains particularly challenging in women with dense breasts who do not otherwise meet high-risk criteria. At our institutions, molecular breast imaging (MBI) is used as supplemental screening for this population. This study evaluated the performance and clinical utility of a mammography-based AI risk model (iCAD ProFound AI Methods: This retrospective IRB-approved study included 416 non-actionable (BI-RADS category 1 or 2) screening digital breast tomosynthesis mammograms (BI-RADS C-D density) obtained from 2018 to 2023, all followed by MBI within one year. The cohort comprised 70 cancer cases (16.8%) and 346 (83.2%) non-cancer controls. Mammograms were retrospectively processed using the ProFound AI Results: Across the full cohort, mean AI risk scores were higher in cancer cases than controls (0.41±0.35 Conclusions: Although overall differences between cancer and non-cancer groups were not statistically significant, the mammography-based AI risk model demonstrated meaningful and statistically significant discrimination in women with extremely dense breasts, outperforming both Tyrer-Cuzick and Gail models. The AI model also showed better separation in ILC and in higher-grade tumors. These findings support the role of image-based AI tools in refining risk assessment in women for whom mammography is least effective and in guiding more targeted use of supplemental MBI screening.
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