ArticleJournal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract2020
Predicting Overall Survival in Patients with Metastatic Rectal Cancer: a Machine Learning Approach.
Article in Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 citations in OpenAlex.
- Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review.Journal of clinical epidemiology · 2023Pooled it
- Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review.BMC medical research methodology · 2022Pooled it
- From regression to machine learning: improving prediction of rectal cancer recurrence.Annals of coloproctology · 2026Article
- Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset.JMIR cancer · 2026Article
- Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model.International journal of medical sciences · 2024Article
- Machine learning in metastatic cancer research: Potentials, possibilities, and prospects.Computational and structural biotechnology journal · 2023Review
- Analysis of Prognostic Factors of Rectal Cancer and Construction of a Prognostic Prediction Model Based on Bayesian Network.Frontiers in public health · 2022Article
- Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort.Cancer management and research · 2022Article
- Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach.Computational and mathematical methods in medicine · 2022Article
- Automated model versus treating physician for predicting survival time of patients with metastatic cancer.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- A non-lab nomogram of survival prediction in home hospice care patients with gastrointestinal cancer.BMC palliative care · 2020Article
- Using machine learning to construct nomograms for patients with metastatic colon cancer.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2020Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundA significant proportion of patients with rectal cancer will present with synchronous metastasis at the time of diagnosis. Overall survival (OS) for these patients are highly variable and previous attempts to build predictive models often have low predictive power, with concordance indexes (c-index) less than 0.70.
methodsUsing the National Cancer Database (2010-2014), we identified patients with synchronous metastatic rectal cancer. The data was split into a training dataset (diagnosis years 2010-2012), which was used to build the machine learning model, and a testing dataset (diagnosis years 2013-2014), which was used to externally validate the model. A nomogram predicting 3-year OS was created using Cox proportional hazard regression with lasso penalization. Predictors were selected based on clinical significance and availability in NCDB. Performance of the machine learning model was assessed by c-index.
resultsA total of 4098 and 3107 patients were used to construct and validate the nomogram, respectively. Internally validated c-indexes at 1, 2, and 3 years were 0.816 (95% CI 0.813-0.818), 0.789 (95% CI 0.786-0.790), and 0.778 (95% CI 0.775-0.780), respectively. External validated c-indexes at 1, 2, and 3 years were 0.811, 0.779, and 0.778, respectively.
conclusionsThere is wide variability in the OS for patients with metastatic rectal cancer, making accurate predictions difficult. However, using machine learning techniques, more accurate models can be built. This will aid patients and clinicians in setting expectations and making clinical decisions in this group of challenging patients.
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