ArticleWorld journal of gastroenterology2025
Predicting chemotherapy-induced myelosuppression in colorectal cancer: An interpretable, machine learning-based nomogram.
Article in World journal of gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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Who cites it
4 citing papers in PubMed.
- Precision management of gastrointestinal tumor-associated osteoporosis driven by cutting-edge technologies: Current status, challenges, and future prospects.World journal of methodology · 2026Review
- Haematological toxicity associated with antineoplastic drugs: a pharmacovigilance analysis based on the FDA adverse event reporting system database.Frontiers in immunology · 2026Article
- Application of explainable artificial intelligence integrating with electronic health record in oncology.Exploration of targeted anti-tumor therapy · 2026Review
- Interpretable machine-learning prediction of severe myelosuppression in colorectal cancer patients receiving chemotherapy using XGBoost and SHAP: a retrospective study with a web-based calculator.Frontiers in oncology · 2026Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer is a common digestive malignancy, and chemotherapy remains a cornerstone of treatment. Myelosuppression, a frequent hematologic toxicity, poses significant clinical challenges. However, no interpretable machine learning-based nomogram exists to predict chemotherapy-induced myelosuppression in colorectal cancer patients. This study aimed to develop and validate an interpretable clinic-machine learning nomogram integrating clinical predictors with multiple algorithms
aimTo develop and validate an interpretable clinic-machine learning nomogram predicting chemotherapy-induced myelosuppression in colorectal cancer.
methodsThis retrospective study enrolled 855 colorectal cancer patients receiving first-line chemotherapy. Data were split into training (
resultsA total of 855 colorectal cancer patients were enrolled, with 765 cases (April 2020 to December 2023) used for model training and validation, and 90 cases (January 2024 to July 2024) for internal testing. Baseline clinical features did not differ significantly between training and validation cohorts (
conclusionThe clinic-machine learning nomogram accurately predicts chemotherapy-induced myelosuppression in colorectal cancer, providing interpretability and clinical utility to support individualized risk assessment and treatment decision-making.
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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 Socratic graph.