Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.

Abstract

BackgroundBreast 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.MethodsWe 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.ResultsFrom 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.DiscussionIntegrating 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.