ReviewJournal of imaging informatics in medicine2026
Non-Invasive Breast Cancer Receptor Typing from Mammograms Using Artificial Intelligence: A Systematic Review and Meta-Analysis.
Review in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
4 authors.
Funding
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Abstract
Artificial intelligence (AI) applied to screening mammography may non-invasively predict breast cancer molecular subtype and receptor status. We conducted a PRISMA-DTA systematic review and bivariate random-effects meta-analysis (PROSPERO CRD420251032810) on this subject. Methods: We conducted a thorough search in MEDLINE, Embase, Scopus, Web of Science, and IEEE Xplore up to May 2025. Eligible studies compared mammogram AI predictions with histopathologic findings. Risk of bias was assessed with the PROBAST tool, and quality assessment was done using transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD). Twenty-five studies met the inclusion criteria. On internal test sets, pooled AUC/sensitivity/specificity were 0.86/84%/80% for luminal subtype, 0.80/70%/78% for HER2-enriched tumors, and 0.76/75%/83% for triple-negative breast cancer. Multi-class receptor-status tasks yielded AUCs: estrogen receptor 0.71, progesterone receptor 0.59, HER2 0.64, and Ki-67 0.60. Binary receptor-status tasks provided AUCs: HER2 0.80 and hormone receptor positive 0.71. Heterogeneity was substantial (I
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Registered trials
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