Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To explore the biological foundations of MRI-based prognostic imaging signatures (including radiomics and deep learning signatures) in breast cancer, and to assess the methodological quality of existing studies. METHODS: This review identified studies through a comprehensive search of PubMed, Embase, Web of Science Core Collection, and the Cochrane Library through February 25, 2025. Studies on MRI-based prognostic radiomics or deep learning models with elaborated biological relevance were included. The Radiomics Quality Score (RQS), Newcastle-Ottawa Scale (NOS), and Quality Assessment of Prognostic Accuracy Studies (QUAPAS) were employed to appraise the quality of studies. Data extraction included details on study characteristics, specifics of radiomics or deep learning models, and methods leveraged for biological analysis. RESULTS: Sixteen studies published from 2015 to 2025, comprising 61-2279 breast cancer patients, were included. Most studies employed supervised machine learning methods, with a few utilizing unsupervised machine learning methods. The underlying biological correlations mainly focused on genomic, tumor microenvironment-related, and multiomics data. The median RQS was 12.5 (range 5-17), and the mean NOS score was 7.3, reflecting limited methodological rigor. The overall risk of bias (ROB) among the studies was high, according to QUAPAS. CONCLUSION: The underlying biological associations of prognostic imaging signatures are mainly elucidated through genomic and transcriptomic factors. Further in-depth exploration is essential to facilitate personalized and precise treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".