ArticleColorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland2020
Using machine learning to construct nomograms for patients with metastatic colon cancer.
Article in Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- MOFR: Multi-omics Feature Reconstruction for Cancer Classification and Metastasis Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Developing a Clinician-Friendly Online Dynamic Nomogram for Survival Prediction in Colon Cancer Patients.Annals of gastroenterological surgery · 2026Article
- Development and validation of an AI-augmented deep learning model for survival prediction in de novo metastatic colorectal cancer.Discover oncology · 2025Article
- Advances in Hereditary Colorectal Cancer: How Precision Medicine Is Changing the Game.Cancers · 2025Review
- A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer.PloS one · 2025Article
- A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.Abdominal radiology (New York) · 2024Article
- A Bayesian network predicting survival of cervical cancer patients-Based on surveillance, epidemiology, and end results.Cancer science · 2023Article
- Machine learning in gastrointestinal surgery.Surgery today · 2022Review
- Nomogram to Predict the Overall Survival of Colorectal Cancer Patients: A Multicenter National Study.International journal of environmental research and public health · 2021Article
- Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
aimPatients with synchronous colon cancer metastases have highly variable overall survival (OS), making accurate predictive models challenging to build. We aim to use machine learning to more accurately predict OS in these patients and to present this predictive model in the form of nomograms for patients and clinicians.
methodsUsing the National Cancer Database (2010-2014), we identified right colon (RC) and left colon (LC) cancer patients with synchronous metastases. Each primary site was split into training and testing datasets. Nomograms predicting 3- year OS were created for each site using Cox proportional hazard regression with lasso regression. Each model was evaluated by both calibration (comparison of predicted vs observed OS) and validation (degree of concordance as measured by the c-index) methodologies.
resultsA total of 11 018 RC and 8346 LC patients were used to construct and validate the nomograms. After stratifying each model into five risk groups, the predicted OS was within the 95% CI of the observed OS in four out of five risk groups for both the RC and LC models. Externally validated c-indexes at 3 years for the RC and LC models were 0.794 and 0.761, respectively.
conclusionsUtilization of machine learning can result in more accurate predictive models for patients with metastatic colon cancer. Nomograms built from these models can assist clinicians and patients in the shared decision-making process of their cancer care.
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