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This paper is in the following e-collection/theme issue:

Cancer Prognosis Models and Machine Learning (82) Secondary Use of Clinical Data for Research and Surveillance (493) Machine Learning (2238) Decision Support for Health Professionals (1644) Clinical Informatics (1449) Electronic Health Records (1383) Innovations and Technology for Cancer Prevention and Screening (212) Colon and Rectal Cancer (54) Children and Young People With Cancer, Pediatric Oncology (89)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64506, first published .
Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study

Predicting Early-Onset Colorectal Cancer in Individuals Below Screening Age Using Machine Learning and Real-World Data: Case Control Study

Authors of this article:

Chengkun Sun1 Author Orcid Image ;   Erin Mobley2, 3 Author Orcid Image ;   Michael Quillen4 Author Orcid Image ;   Max Parker4 Author Orcid Image ;   Meghan Daly3 Author Orcid Image ;   Rui Wang5 Author Orcid Image ;   Isabela Visintin3 Author Orcid Image ;   Ziad Awad3 Author Orcid Image ;   Jennifer Fishe6 Author Orcid Image ;   Alexander Parker2, 7 Author Orcid Image ;   Thomas George2, 4 Author Orcid Image ;   Jiang Bian8 Author Orcid Image ;   Jie Xu1 Author Orcid Image
Article Authors Cited by (3) Tweetations (1) Metrics

Journals

  1. Le D, Pham T. Colorectal cancer worldwide: epidemiological trends, economic burden, and the promise of AI-driven solutions. Exploration of Medicine 2025;6 View
  2. Ni W, Zhang B, Gao Y, Jiang J, Ye Y, Chen J, Lin X, Yu H, Wang L, Xiao C. Explainable machine learning model for predicting early recurrence and distant metastasis after surgery in early-onset colorectal cancer. Surgery 2025:109973 View
  3. Ajjawi I, Kim I, Smani S, Palencia P, Diaz G, Lee W, Kim I, Sprenkle P, Leapman M. Machine learning approaches to optimize the integration of sociodemographic factors for predicting cancer-specific survival among patients with high-risk prostate cancer. Current Urology 2026 View