{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T14:11:32Z","timestamp":1785247892225,"version":"3.55.0"},"reference-count":89,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Institucion Universitaria ITM"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Technol Cancer Res Treat"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Breast cancer molecular subtypes (Luminal A, luminal B, HER2-positive, and triple-negative) are typically determined through biopsy and immunohistochemistry, procedures that are invasive and prone to interpretative variability. Artificial intelligence applied to medical imaging has emerged as a non-invasive alternative to support subtype prediction. However, methodological rigor, risk of bias and clinical translatability of existing studies remain insufficiently characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a systematic review in accordance with the PRISMA 2020 guidelines. Searches were performed in Scopus, Web of Science, ScienceDirect, and PubMed to identify English-language, peer-reviewed original articles published between January 1, 2020, and March 25, 2025. Two reviewers independently screened titles and abstracts using Rayyan, retrieved potentially eligible full-text articles, and assessed risk of bias and applicability using an AI-adapted QUADAS framework based on QUADAS-2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>From 626 identified records, 76 full-text articles were assessed for eligibility. The AI-adapted QUADAS framework showed that 67 of these 76 articles were excluded because they presented a high or unclear risk of bias in at least one evaluated domain. Consequently, only 9 studies were judged to have a low risk of bias and were included in the qualitative synthesis. The included evidence was dominated by studies using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with additional contributions from ultrasound-based approaches. The methodological strategies ranged from radiomics-based machine learning models to deep learning architectures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Integrating radiomic signatures with clinical predictors may improve performance in selected clinical scenarios, and peri-tumoral information alongside delayed DCE-MRI phases can contribute complementary diagnostic value. Nevertheless, the small number of low-risk studies highlights the need for standardized acquisition and preprocessing reporting, patient-level data splitting, robust external validation, and broader data availability to strengthen the clinical translation and generalizability of AI-based molecular subtyping.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/15330338261465062","type":"journal-article","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T10:55:01Z","timestamp":1784976901000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8672-9557","authenticated-orcid":false,"given":"Juan 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