ArticleCancers2019
Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis.Asian Pacific journal of cancer prevention : APJCP · 2023Pooled it
- Machine learning application in colon cancer treatment outcome prediction.Scientific reports · 2026Article
- Role of circAGFG1 as an oncogene in triple-negative breast cancer.Apoptosis : an international journal on programmed cell death · 2026Article
- Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.Oncology research · 2026Article
- Fourier transform mid-infrared imaging and rapid evaporative ionization mass spectrometry imaging in FFPE colorectal adenocarcinoma samples.Pathologie (Heidelberg, Germany) · 2026Review
- Machine learning-based survival prediction in colorectal cancer combining clinical and biological features.Oncotarget · 2025Article
- Value of enhanced CT machine learning models combined with clinicoradiological characteristics in predicting lymphatic tissue metastasis in colon cancer.Radiologie (Heidelberg, Germany) · 2025Article
- Expression of VISTA on T cells in patients with cancer colon.Journal of the Egyptian National Cancer Institute · 2025Article
- Applications of Artificial Intelligence for Metastatic Gastrointestinal Cancer: A Systematic Literature Review.Cancers · 2025Review
- The diagnostic and prognostic value ofBioImpacts : BI · 2025Article
- Development and Validation of Machine Learning Model Platelet Index-based Predictor for Colorectal Cancer Stage.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Colorectal cancer prognosis based on dietary pattern using synthetic minority oversampling technique with K-nearest neighbors approach.Scientific reports · 2024Article
- Enhancing Interpretability in Medical Image Classification by Integrating Formal Concept Analysis with Convolutional Neural Networks.Biomimetics (Basel, Switzerland) · 2024Article
- Comprehensive Analysis of the Function and Prognostic Value of TAS2Rs Family-Related Genes in Colon Cancer.International journal of molecular sciences · 2024Article
- A nomogram based on MRI radiomics features of mesorectal fat for diagnosing T2- and T3-stage rectal cancer.Abdominal radiology (New York) · 2024Article
- Identification and clinicopathological analysis of potential p73-regulated biomarkers in colorectal cancer via integrative bioinformatics.Scientific reports · 2024Article
- Machine learning-based classifiers to predict metastasis in colorectal cancer patients.Frontiers in artificial intelligence · 2024Article
- Classification of Long Non-Coding RNAs s Between Early and Late Stage of Liver Cancers From Non-coding RNA Profiles Using Machine-Learning Approach.Bioinformatics and biology insights · 2024Article
- A comprehensive view on the apigenin impact on colorectal cancer: Focusing on cellular and molecular mechanisms.Food science & nutrition · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.
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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 OpenQuestion graph.