An Ensemble Prognostic Model for Colorectal Cancer

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...

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Main Authors:	Li, Bi-Qing, Huang, Tao, Zhang, Jian, Zhang, Ning, Huang, Guo-Hua, Liu, Lei, Cai, Yu-Dong
Format:	Online
Language:	English
Published:	Public Library of Science 2013
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3642113/

id	pubmed-3642113
recordtype	oai_dc
spelling	pubmed-36421132013-05-08 An Ensemble Prognostic Model for Colorectal Cancer Li, Bi-Qing Huang, Tao Zhang, Jian Zhang, Ning Huang, Guo-Hua Liu, Lei Cai, Yu-Dong Research Article 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. Public Library of Science 2013-05-02 /pmc/articles/PMC3642113/ /pubmed/23658834 http://dx.doi.org/10.1371/journal.pone.0063494 Text en © 2013 Li et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Li, Bi-Qing Huang, Tao Zhang, Jian Zhang, Ning Huang, Guo-Hua Liu, Lei Cai, Yu-Dong
spellingShingle	Li, Bi-Qing Huang, Tao Zhang, Jian Zhang, Ning Huang, Guo-Hua Liu, Lei Cai, Yu-Dong An Ensemble Prognostic Model for Colorectal Cancer
author_facet	Li, Bi-Qing Huang, Tao Zhang, Jian Zhang, Ning Huang, Guo-Hua Liu, Lei Cai, Yu-Dong
author_sort	Li, Bi-Qing
title	An Ensemble Prognostic Model for Colorectal Cancer
title_short	An Ensemble Prognostic Model for Colorectal Cancer
title_full	An Ensemble Prognostic Model for Colorectal Cancer
title_fullStr	An Ensemble Prognostic Model for Colorectal Cancer
title_full_unstemmed	An Ensemble Prognostic Model for Colorectal Cancer
title_sort	ensemble prognostic model for colorectal cancer
description	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.
publisher	Public Library of Science
publishDate	2013
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3642113/
_version_	1611974434015412224

An Ensemble Prognostic Model for Colorectal Cancer

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