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An Ensemble Prognostic Model for Colorectal Cancer

Author

Listed:
  • Bi-Qing Li
  • Tao Huang
  • Jian Zhang
  • Ning Zhang
  • Guo-Hua Huang
  • Lei Liu
  • Yu-Dong Cai

Abstract

Colorectal cancer can be grouped into Dukes A, B, C, and D stages based on its developments. Generally speaking, more advanced patients have poorer prognosis. To integrate progression stage prediction systems with recurrence prediction systems, we proposed an ensemble prognostic model for colorectal cancer. In this model, each patient was assigned a most possible stage and a most possible recurrence status. If a patient was predicted to be recurrence patient in advanced stage, he would be classified into high risk group. The ensemble model considered both progression stages and recurrence status. High risk patients and low risk patients predicted by the ensemble model had a significant different disease free survival (log-rank test p-value, 0.0016) and disease specific survival (log-rank test p-value, 0.0041). The ensemble model can better distinguish the high risk and low risk patients than the stage prediction model and the recurrence prediction model alone. This method could be applied to the studies of other diseases and it could significantly improve the prediction performance by ensembling heterogeneous information.

Suggested Citation

  • Bi-Qing Li & Tao Huang & Jian Zhang & Ning Zhang & Guo-Hua Huang & Lei Liu & Yu-Dong Cai, 2013. "An Ensemble Prognostic Model for Colorectal Cancer," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    • Handle: RePEc:plo:pone00:0063494
    DOI: 10.1371/journal.pone.0063494
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    References listed on IDEAS

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    1. Tao Huang & Lei Chen & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
    2. Tao Huang & WeiRen Cui & LeLe Hu & KaiYan Feng & Yi-Xue Li & Yu-Dong Cai, 2009. "Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, December.
    3. Bi-Qing Li & Le-Le Hu & Lei Chen & Kai-Yan Feng & Yu-Dong Cai & Kuo-Chen Chou, 2012. "Prediction of Protein Domain with mRMR Feature Selection and Analysis," PLOS ONE, Public Library of Science, vol. 7(6), pages 1-14, June.
    4. Bi-Qing Li & Yu-Dong Cai & Kai-Yan Feng & Gui-Jun Zhao, 2012. "Prediction of Protein Cleavage Site with Feature Selection by Random Forest," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-9, September.
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