SynthesisJournal of translational medicine2025
Deciphering the biological underpinnings behind prognostic MRI-based imaging signatures in breast cancer: a systematic review.
Synthesis in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Letter to the Editor: A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.European radiology · 2026Article
- AI-mediated ultrasound radiomics in the diagnosis and treatment of triple-negative breast cancer: research progress and future challenges.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveTo 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.
methodsThis 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.
resultsSixteen 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.
conclusionThe 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.
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.