ArticleCancer medicine2017
Evaluation of a prediction model for colorectal cancer: retrospective analysis of 2.5 million patient records.
Article in Cancer medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 6 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Diagnostic Prediction Models for Primary Care, Based on AI and Electronic Health Records: Systematic Review.JMIR medical informatics · 2025Pooled it
- Clinical Prediction Models Incorporating Blood Test Trend for Cancer Detection: Systematic Review, Meta-Analysis, and Critical Appraisal.JMIR cancer · 2025Pooled it
- Efficacy of ColonFlag as a Complete Blood Count-Based Machine Learning Algorithm for Early Detection of Colorectal Cancer: A Systematic Review.Iranian journal of medical sciences · 2024Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Pooled it
- Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- The use of blood test trends in cancer detection: a scoping review.Diagnostic and prognostic research · 2026Review
- The Value of Clinical Decision Support in Healthcare: A Focus on Screening and Early Detection.Diagnostics (Basel, Switzerland) · 2025Review
- Review
- Construction and validation of a postoperative hypothermia prediction model in elderly patients undergoing colorectal surgery.Heliyon · 2024Article
- Complete Blood Count Alterations Prior to the Diagnosis of Colorectal Cancer May Help in the Detection of Synchronous Liver Metastases.Journal of clinical medicine · 2023Article
- The performance of FIT-based and other risk prediction models for colorectal neoplasia in symptomatic patients: a systematic review.EClinicalMedicine · 2023Review
- Article
- Bayesian risk prediction model for colorectal cancer mortality through integration of clinicopathologic and genomic data.NPJ precision oncology · 2023Article
- Machine Learning in Colorectal Cancer Risk Prediction from Routinely Collected Data: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- BLOod Test Trend for cancEr Detection (BLOTTED): protocol for an observational and prediction model development study using English primary care electronic health record data.Diagnostic and prognostic research · 2023Article
- Article
- Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review.JMIR medical informatics · 2022Review
- The Full Blood Count Blood Test for Colorectal Cancer Detection: A Systematic Review, Meta-Analysis, and Critical Appraisal.Cancers · 2020Review
- Data mining-based model and risk prediction of colorectal cancer by using secondary health data: A systematic review.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Earlier detection of colorectal cancer greatly improves prognosis, largely through surgical excision of neoplastic polyps. These include benign adenomas which can transform over time to malignant adenocarcinomas. This progression may be associated with changes in full blood count indices. An existing risk algorithm derived in Israel stratifies individuals according to colorectal cancer risk using full blood count data, but has not been validated in the UK. We undertook a retrospective analysis using the Clinical Practice Research Datalink. Patients aged over 40 with full blood count data were risk-stratified and followed up for a diagnosis of colorectal cancer over a range of time intervals. The primary outcome was the area under the receiver operating characteristic curve for the 18-24-month interval. We also undertook a case-control analysis (matching for age, sex, and year of risk score), and a cohort study of patients undergoing full blood count testing during 2012, to estimate predictive values. We included 2,550,119 patients. The area under the curve for the 18-24-month interval was 0.776 [95% confidence interval (CI): 0.771, 0.781]. Performance improves as the time interval reduces. The area under the curve for the age-matched case-control analysis was 0.583 [0.574, 0.591]. For the population risk-scored in 2012, the positive predictive value at 99.5% specificity was 8.8% with negative predictive value 99.6%. The algorithm offers an additional means of identifying risk of colorectal cancer, and could support other approaches to early detection, including screening and active case finding.
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