ArticleWorld journal of gastroenterology2024
Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.
Article in World journal of gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- Machine learning and deep learning models for predicting colorectal cancer metastases: A comprehensive review.European journal of radiology open · 2026Review
- The clinical application value of body composition in predicting the prognosis of rectal cancer.International journal of clinical oncology · 2026Article
- Development and validation of an interpretable prediction model for the risk of unplanned reoperation in patients underwent intracranial tumor surgery.Scientific reports · 2026Article
- Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.World journal of gastroenterology · 2025Article
- Long-term prognosis and risk factors associated with 30-day unplanned repeated ERCP in patients with common bile duct stones.Therapeutic advances in gastroenterology · 2025Article
- Association between the red cell distribution width-to-albumin ratio and recurrence-free survival and overall survival in patients with non-muscle-invasive bladder cancer: a retrospective cohort study.Frontiers in oncology · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical data.
aimTo develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.
methodsData of patients treated for colorectal cancer (
resultsMore patients in the experimental group were ≥ 60 years old, male, and had a history of hypertension, laparotomy, and hypoproteinemia, compared to the control group. Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation (
conclusionThis study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer, which can improve treatment decisions and prognosis.
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