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

Pushpanjali Gupta1, Sum-Fu Chiang2,3, Prasan Kumar Sahoo1,2

  • 1Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan.

Cancers
|December 18, 2019
PubMed
Summary

Machine learning accurately predicts colon cancer TNM staging and five-year disease-free survival. The Random Forest model, incorporating the Tumor Aggression Score, showed superior performance in this clinical research.

Keywords:
TNM stagingartificial intelligencecolon cancerdisease-free survivalmachine learningprediction

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Area of Science:

  • Oncology
  • Medical Informatics
  • Computational Biology

Background:

  • Accurate tumor staging is crucial for colon cancer prognosis and treatment planning.
  • Histopathology parameters and machine learning offer potential for improved predictive accuracy.

Purpose of the Study:

  • To predict tumor stage in colon cancer using key histopathology features and machine learning.
  • To predict five-year disease-free survival (DFS) in colon cancer patients using machine learning.
  • To evaluate the impact of the Tumor Aggression Score (TAS) on predictive model performance.

Main Methods:

  • Utilized a dataset of 4021 patients from the colorectal cancer (CRC) registry.
  • Applied various machine learning (ML) algorithms for tumor staging and DFS prediction.
  • Evaluated model performance using five-fold cross-validation.
  • Incorporated standard TNM staging attributes and the Tumor Aggression Score (TAS).

Main Results:

  • The Random Forest model achieved an F-measure of 0.89 when TAS was included for TNM staging.
  • Random Forest demonstrated the highest accuracy (approx. 84%) and AUC (0.82 ± 0.10) for predicting five-year DFS.
  • The inclusion of TAS improved the predictive performance of ML models.

Conclusions:

  • Machine learning, particularly the Random Forest algorithm, can effectively predict colon cancer TNM staging and DFS.
  • The Tumor Aggression Score is a valuable prognostic factor that enhances ML-based prediction models.
  • These findings support the integration of ML and novel biomarkers in clinical oncology research for improved patient outcomes.