ArticlePloS one2012
Which is a more accurate predictor in colorectal survival analysis? Nine data mining algorithms vs. the TNM staging system.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in colorectal surgery: an AI-powered systematic review.Techniques in coloproctology · 2023Pooled it
- Systematic review and meta-analysis of the impact of tumour budding in colorectal cancer.British journal of cancer · 2016Pooled it
- Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study.JMIR medical informatics · 2025Article
- Survival prediction from imbalanced colorectal cancer dataset using hybrid sampling methods and tree-based classifiers.Scientific reports · 2025Article
- Classification and Diagnostic Prediction of Colorectal Cancer Mortality Based on Machine Learning Algorithms: A Multicenter National Study.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital.Asian Pacific journal of cancer prevention : APJCP · 2020Article
- The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning.Scientific reports · 2020Article
- Predicting the effect of 5-fluorouracil-based adjuvant chemotherapy on colorectal cancer recurrence: A model using gene expression profiles.Cancer medicine · 2020Article
- Integrative Analysis of CD133 mRNA in Human Cancers Based on Data Mining.Stem cell reviews and reports · 2019Review
- Evolving notions on immune response in colorectal cancer and their implications for biomarker development.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2018Review
- Comparison of Basic and Ensemble Data Mining Methods in Predicting 5-Year Survival of Colorectal Cancer Patients.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2017Article
- Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.Computers in biology and medicine · 2015Article
- Mining causal relationships among clinical variables for cancer diagnosis based on Bayesian analysis.BioData mining · 2015Article
- Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry.BMJ open · 2014Article
- T4 Colon Cancer - Current Management.Current health sciences journalArticle
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOver the past decades, many studies have used data mining technology to predict the 5-year survival rate of colorectal cancer, but there have been few reports that compared multiple data mining algorithms to the TNM classification of malignant tumors (TNM) staging system using a dataset in which the training and testing data were from different sources. Here we compared nine data mining algorithms to the TNM staging system for colorectal survival analysis.
methodsTwo different datasets were used: 1) the National Cancer Institute's Surveillance, Epidemiology, and End Results dataset; and 2) the dataset from a single Chinese institution. An optimization and prediction system based on nine data mining algorithms as well as two variable selection methods was implemented. The TNM staging system was based on the 7(th) edition of the American Joint Committee on Cancer TNM staging system.
resultsWhen the training and testing data were from the same sources, all algorithms had slight advantages over the TNM staging system in predictive accuracy. When the data were from different sources, only four algorithms (logistic regression, general regression neural network, bayesian networks, and Naïve Bayes) had slight advantages over the TNM staging system. Also, there was no significant differences among all the algorithms (p>0.05).
conclusionsThe TNM staging system is simple and practical at present, and data mining methods are not accurate enough to replace the TNM staging system for colorectal cancer survival prediction. Furthermore, there were no significant differences in the predictive accuracy of all the algorithms when the data were from different sources. Building a larger dataset that includes more variables may be important for furthering predictive accuracy.
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