ArticleBMJ open2015
Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study.
Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05357508 (Protocol Title), which is not on this map. Cited by 104 papers, 10 of them syntheses that pooled it.
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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.
Protocol Title: Colorectal Cancer Screening Decisions Based on Predicted Risk: the PREcision ScreENing Randomized Controlled Trial (PRESENT)
Who cites it
104 citing papers in PubMed, 10 syntheses or guidelines pooled it.
- Prediction models for early detection and diagnosis of lung cancer in people who have never smoked: a systematic review and critical appraisal.Cancer causes & control : CCC · 2026Pooled it
- Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe.BMC cancer · 2023Pooled it
- Development and internal-external validation of statistical and machine learning models for breast cancer prognostication: cohort study.BMJ (Clinical research ed.) · 2023Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- Reporting of prognostic clinical prediction models based on machine learning methods in oncology needs to be improved.Journal of clinical epidemiology · 2021Pooled it
- Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review.Advances in therapy · 2021Pooled it
- Understanding the role of GPs' gut feelings in diagnosing cancer in primary care: a systematic review and meta-analysis of existing evidence.The British journal of general practice : the journal of the Royal College of General Practitioners · 2020Pooled it
- Pooled it
- Light Alcohol Drinking and Risk of Cancer: A Meta-Analysis of Cohort Studies.Cancer research and treatment · 2018Pooled it
- Electronic clinical decision support tool for assessing stomach symptoms in primary care (ECASS): a feasibility study.BMJ open · 2021Trial
- Prostate-specific antigen testing and opportunistic prostate cancer screening: a cohort study in England, 1998-2017.The British journal of general practice : the journal of the Royal College of General Practitioners · 2021Trial
- QResearch primary care records linked to national hospital and cancer registry data: a validation study.ESMO real world data and digital oncology · 2026Article
- Managing Refractory Pericarditis in the Era of Biologic Therapy: A Cardio-Oncology-Based Risk Framework.JACC. Advances · 2026Article
- Machine learning using PROFUND components for 30-day readmission prediction in multimorbid patients: a prospective multicentre study.Scientific reports · 2026Article
- Leveraging the Electronic Health Record for Early Detection of Pancreatic Cancer Among 9.4 Million US Veterans.Clinical and translational gastroenterology · 2026Article
- Decision support tools for pancreatic cancer detection: external validation in Australian primary care - a retrospective cohort study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
- Risk-based guidance for choosing fecal immunochemical test or colonoscopy in colorectal cancer screening: a modeling study.American journal of epidemiology · 2026Article
- Oral contraceptives exposure may reduce the risk of ovarian cancer: a meta-analysis based on cohort studies.Frontiers in pharmacology · 2026Article
- Impact of cancer outcome data source on the diagnostic accuracy of ovarian cancer prediction models: a primary care cohort study.BMJ public health · 2026Article
44 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo derive and validate a set of clinical risk prediction algorithm to estimate the 10-year risk of 11 common cancers.
designProspective open cohort study using routinely collected data from 753 QResearch general practices in England. We used 565 practices to develop the scores and 188 for validation. SUBJECTS: 4.96 million patients aged 25-84 years in the derivation cohort; 1.64 million in the validation cohort. Patients were free of the relevant cancer at baseline.
methodsCox proportional hazards models in the derivation cohort to derive 10-year risk algorithms. Risk factors considered included age, ethnicity, deprivation, body mass index, smoking, alcohol, previous cancer diagnoses, family history of cancer, relevant comorbidities and medication. Measures of calibration and discrimination in the validation cohort. OUTCOMES: Incident cases of blood, breast, bowel, gastro-oesophageal, lung, oral, ovarian, pancreas, prostate, renal tract and uterine cancers. Cancers were recorded on any one of four linked data sources (general practitioner (GP), mortality, hospital or cancer records).
resultsWe identified 228,241 incident cases during follow-up of the 11 types of cancer. Of these 25,444 were blood; 41,315 breast; 32,626 bowel, 12,808 gastro-oesophageal; 32,187 lung; 4811 oral; 6635 ovarian; 7119 pancreatic; 35,256 prostate; 23,091 renal tract; 6949 uterine cancers. The lung cancer algorithm had the best performance with an R(2) of 64.2%; D statistic of 2.74; receiver operating characteristic curve statistic of 0.91 in women. The sensitivity for the top 10% of women at highest risk of lung cancer was 67%. Performance of the algorithms in men was very similar to that for women.
conclusionsWe have developed and validated a prediction models to quantify absolute risk of 11 common cancers. They can be used to identify patients at high risk of cancers for prevention or further assessment. The algorithms could be integrated into clinical computer systems and used to identify high-risk patients. WEB CALCULATOR: There is a simple web calculator to implement the Qcancer 10 year risk algorithm together with the open source software for download (available at http://qcancer.org/10yr/).
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