ArticleNPJ precision oncology2024
Development and validation of machine learning models for young-onset colorectal cancer risk stratification.
Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06342622 (Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Application of Artificial Intelligence for Young-onset Colorectal Cancer Screening Based on Electronic Medical Records
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics for predicting microsatellite instability-high status in colorectal cancer: a systematic review and meta-analysis.World journal of surgical oncology · 2026Pooled it
- Time-updated explainable machine learning predicts short-term mortality in peritoneal dialysis patients.Renal failure · 2026Article
- Artificial intelligence for the prediction of prognosis in colorectal cancer patients using routine blood indices.NPJ digital medicine · 2026Article
- Artificial Intelligence in Population-Level Gastroenterology and Hepatology: A Comprehensive Review of Public Health Applications and Quantitative Impact.Digestive diseases and sciences · 2026Review
- Knowledge, Symptom Awareness, and Referral Practices of Primary Care Providers in Early-Onset Colorectal Cancer.Population health management · 2026Article
- Interpretable Machine Learning Models Based on Blood Cell-Derived Inflammatory Indices for Identifying Colorectal Neoplasia: A Retrospective Study.Journal of inflammation research · 2026Article
- Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.Frontiers in immunology · 2026Review
- Development and Validation of an Interpretable Machine Learning Model Based on Peripheral Blood Biomarkers for Esophageal Cancer Risk Prediction.International journal of general medicine · 2026Article
- Higher local recurrence as the distinct failure pattern in early-onset rectal cancer: a tailored MRI score to guide therapy.NPJ precision oncology · 2025Article
- Constructing multicancer risk cohorts using national data from medical helplines and secondary care.NPJ digital medicine · 2025Article
- Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.World journal of gastroenterology · 2025Article
- Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis.Scientific reports · 2025Article
- Predicting Early-Onset Colorectal Cancer with Large Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Incidence of young-onset colorectal cancer (YOCRC, younger than 50) has significantly increased worldwide. The performance of fecal immunochemical test in detecting YOCRC is unsatisfactory. Using routine clinical data, we aimed to develop machine learning (ML) models to identify individuals with high-risk YOCRC who require further colonoscopy. We retrospectively extracted data of 10,874 young individuals. Multiple supervised ML techniques were devised to distinguish individuals with and without CRC, classifiers were trained, internally validated and temporally validated. In internal validation cohort, Random Forest (RF) ML model demonstrated good performance with AUC of 0.859 and highest recall of 0.840. In temporal validation cohort, the RF ML model also exhibited good classification performance, achieving AUC of 0.888 and highest recall of 0.872. RF algorithm-based approach is effective and feasible in YOCRC risk stratification. This could be valuable in assessing the risk of YOCRC so that clinical management, including further colonoscopy, can be subsequently made. (Registration: This study was registered with ClinicalTrials.gov (NCT06342622) on March 15, 2024.).
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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.