ArticleDigestive diseases and sciences2017
Early Colorectal Cancer Detected by Machine Learning Model Using Gender, Age, and Complete Blood Count Data.
Article in Digestive diseases and sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 62 papers, 5 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.
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Who cites it
62 citing papers in PubMed, 5 syntheses or guidelines pooled it, 123 citations in OpenAlex.
- 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
- Pooled it
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled 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
- Development and external validation of a machine-learning risk model for colorectal cancer triage in Sweden's fast-track pathway.BMC medical informatics and decision making · 2026Article
- Examining the gene network and prognostic biomarkers in the onset of colorectal cancer in stool samples using machine learning.Discover oncology · 2026Article
- Applying Supervised Machine Learning to Effusion Analysis for the Diagnosis of Feline Infectious Peritonitis.Bioengineering (Basel, Switzerland) · 2026Article
- Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.Oncology research · 2026Article
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Utilizing an Artificial Intelligence and Machine Learning Model to Predict Colorectal Cancer Risk in American Samoa: A Pilot Study.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Review
- Article
- Review
- Exploration of the application potential of serum multi-biomarker model in colorectal cancer screening.Scientific reports · 2024Article
- Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Assessing the feasibility of applying machine learning to diagnosing non-effusive feline infectious peritonitis.Scientific reports · 2024Article
- Machine Learning as a Tool for Early Detection: A Focus on Late-Stage Colorectal Cancer across Socioeconomic Spectrums.Cancers · 2024Article
- Blood-based biomarkers and novel technologies for the diagnosis of colorectal cancer and adenomas: a narrative review.Biomarkers in medicine · 2024Review
2 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning tools identify patients with blood counts indicating greater likelihood of colorectal cancer and warranting colonoscopy referral.
aimsTo validate a machine learning colorectal cancer detection model on a US community-based insured adult population.
methodsEligible colorectal cancer cases (439 females, 461 males) with complete blood counts before diagnosis were identified from Kaiser Permanente Northwest Region's Tumor Registry. Control patients (n = 9108) were randomly selected from KPNW's population who had no cancers, received at ≥1 blood count, had continuous enrollment from 180 days prior to the blood count through 24 months after the count, and were aged 40-89. For each control, one blood count was randomly selected as the pseudo-colorectal cancer diagnosis date for matching to cases, and assigned a "calendar year" based on the count date. For each calendar year, 18 controls were randomly selected to match the general enrollment's 10-year age groups and lengths of continuous enrollment. Prediction performance was evaluated by area under the curve, specificity, and odds ratios.
resultsArea under the receiver operating characteristics curve for detecting colorectal cancer was 0.80 ± 0.01. At 99% specificity, the odds ratio for association of a high-risk detection score with colorectal cancer was 34.7 (95% CI 28.9-40.4). The detection model had the highest accuracy in identifying right-sided colorectal cancers.
conclusionsColonFlag
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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.