SynthesisGut2019
Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies.
Synthesis in Gut, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 5 of them syntheses that pooled it.
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
30 citing papers in PubMed, 5 syntheses or guidelines pooled it, 50 citations in OpenAlex.
- Systematic Review and Meta-Analysis: Taurine and Its Association With Colorectal Carcinoma.Cancer medicine · 2024Pooled it
- [Cancer prevention recommendations: Update 2024].Atencion primaria · 2024Guideline
- Circulating Sex Hormone Levels and Colon Cancer Risk in Men: A Nested Case-Control Study and Meta-Analysis.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Pooled it
- Risk prediction models for esophageal cancer: A systematic review and critical appraisal.Cancer medicine · 2021Pooled it
- Pooled it
- Shaping the future of early-onset colorectal cancer prevention.Nature reviews. Cancer · 2026Review
- Validation of a Colorectal Cancer Risk Prediction Model in US Black Women.Cancer prevention research (Philadelphia, Pa.) · 2026Article
- Early risk stratification of sepsis-related liver injury via machine learning: a multicohort study.Frontiers in medicine · 2026Article
- Article
- Stratified/risk-based screening for colorectal cancer in the UK: an overview.Colorectal cancer · 2025Review
- External validation of the colorectal cancer risk score LiFeCRC using food frequency questions in the HUNT study.International journal of colorectal disease · 2024Article
- Outcomes of prognostication in people living with advanced cancer: A qualitative study to inform a Core Outcome Set.PloS one · 2024Article
- Lifestyle predictors of colorectal cancer in European populations: a systematic review.BMJ nutrition, prevention & health · 2024Article
- Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk.Breast cancer research and treatment · 2023Article
- Colorectal Cancer Risk Assessment and Precision Approaches to Screening: Brave New World or Worlds Apart?Gastroenterology · 2023Review
- Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- [PAPPS Expert Groups. Cancer prevention recommendations: Update 2022].Atencion primaria · 2022Article
- Five-year absolute risk estimates of colorectal cancer based on CCRAT model and polygenic risk scores: A validation study using the Quebec population-based cohort CARTaGENE.Preventive medicine reports · 2022Article
- Machine Learning Models Cannot Replace Screening Colonoscopy for the Prediction of Advanced Colorectal Adenoma.Journal of personalized medicine · 2021Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
37 authors at 20 institutions in 11 countries.
Funding
Abstract
objectiveTo systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.
designModels were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).
resultsThe systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.
conclusionSeveral of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.
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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.