ArticleFrontiers in oncology2023
A nomogram model for predicting distant metastasis of newly diagnosed colorectal cancer based on clinical features.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Article
- Machine learning for predicting liver metastasis in colorectal cancer.Discover oncology · 2026Article
- Development and internal validation of an immune-based prognostic modeling of early-onset colorectal cancerWorld journal of clinical oncology · 2026Article
- OncoE25: an AI model for predicting postoperative prognosis in early-onset stage I-III colon and rectal cancer-a population-based study using SEER with dual-center cohort validation.Journal of translational medicine · 2025Article
- Cerebellar metastasis from colorectal cancer: a case report.Frontiers in oncology · 2025Article
- Predicting colorectal cancer risk: a novel approach using anemia and blood test markers.Frontiers in oncology · 2024Article
- Using machine learning approach for screening metastatic biomarkers in colorectal cancer and predictive modeling with experimental validation.Scientific reports · 2023Article
- Prediction of liver and lung metastases in patients with early-onset colorectal cancer by nomograms based on heterogeneous and homogenous risk factors.Cancer medicine · 2023Article
- A machine learning-based model for predicting distant metastasis in patients with rectal cancer.Frontiers in oncology · 2023Article
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7 authors.
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Abstract
Objective: Colorectal cancer is one of the most common primary malignancies and the third most common cause of cancer death in both men and women in the United States. Among people diagnosed with initial colorectal cancer, 22% had metastatic colorectal cancer, while the 5-year survival rate was less than 20%. The purpose of this study is to develop a nomogram for predicting distant metastasis in newly diagnosed colorectal cancer patients and to identify high-risk groups. Methods: We retrospectively reviewed the data of patients who were diagnosed with colorectal cancer at Zhong nan Hospital of Wuhan University and People's Hospital of Gansu Province between January 2016 and December 2021. Risk predictors for distant metastasis from colorectal patients were determined by the univariate and multivariate logistic regression analyses. Nomograms were developed to predict the probabilities of distant metastatic sites of colorectal cancer patients and evaluated by calibration curves, receiver operating characteristic curves, and decision curve analysis (DCA). Results: A total of 327 cases were included in this study: 224 colorectal cancer patients from Zhong nan Hospital of Wuhan University were incorporated into the training set, and 103 colorectal cancer patients from Gansu Provincial People's Hospital were incorporated into the testing set. By univariate logistic regression analysis, platelet (PLT) level ( Conclusion: The nomogram showed excellent accuracy in predicting distant metastatic sites, and clinical utility may facilitate clinical decision-making.
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