Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach.

Clicks: 336
ID: 71314
2019
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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.
Reference Key
gupta2019predictioncancers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gupta, Pushpanjali;Chiang, Sum-Fu;Sahoo, Prasan Kumar;Mohapatra, Suvendu Kumar;You, Jeng-Fu;Onthoni, Djeane Debora;Hung, Hsin-Yuan;Chiang, Jy-Ming;Huang, Yenlin;Tsai, Wen-Sy;
Journal Cancers
Year 2019
DOI
E2007
URL
Keywords
artificial intelligence Machine learning prediction colon cancer disease-free survival tnm staging

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