SynthesisBMC cancer2025
A systematic review of prediction models for risk of breast cancer.
Synthesis in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Considering the caregiver in early-phase clinical trials: a review of trial protocols and consent forms.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Review
- Development and External Validation of an Interpretable Machine Learning Model Using Routine Blood Biomarkers for Differentiating Malignant Breast Nodules.Journal of inflammation research · 2026Article
- Integrating Serum Lipid Biomarkers Into Machine Learning for the Differential Diagnosis of Breast Nodules.The breast journal · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the risk of breast cancer (BC) can help with early diagnosis, treatment, and prevention strategies. Existing BC risk prediction models typically utilize demographic, genetic, and/or imaging-derived variables. This systematic review summarizes all developed models for BC risk in general and high-risk populations, and their discriminatory ability and calibration.
methodsMEDLINE and Embase were searched for studies developing and/or validating models estimating risk of developing BC in women and/or men. After removal of duplicates, 9,511 titles and abstracts were screened, yielding 360 full-text reviews. The current review focuses on all studies which developed a new model to predict BC risk in general and high-risk populations.
resultsA total of 107 studies developing new models for BC risk prediction were included in this review. Sample sizes ranged from 535 to 2,392,998 for cohort studies, and from 133 cases with 113 controls to 95,075 cases with 75,017 controls for case-control studies. Areas under the receiver-operating characteristic curves (AUCs) ranged from 0.51 to 0.96. For studies reporting calibration using observed/expected events (O/E) ratio (n = 8), the range was 0.84 to 1.10. External validation was reported in 18 studies.
conclusionsMost BC risk models were developed in Caucasian populations, and their predictive ability and quality varied. Models including both demographic and genetic or imaging/biopsy data performed better than models based on demographic variables alone. However, their performance was not further improved by the addition of multiple types of such data.
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What Socratic 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 Socratic graph.