SynthesisTechnology in cancer research & treatment
Artificial Intelligence for Breast Cancer Molecular Subtype Prediction From Medical Imaging: A Systematic Review of Literature.
Synthesis in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.