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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
Developing Prognostic Systems of Cancer Patients by Ensemble Clustering
Journal of biomedicine & biotechnology, v 2009, pp 632786-7
01 Jan 2009
PMID: 19584918
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Accurate prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system that involves only three factors: tumor extent, lymph node involvement, and metastasis. This prediction from the TNM has been limited, because other potential prognostic factors are not used in the system. Based on availability of large cancer datasets, it is possible to establish powerful prediction systems by using machine learning procedures and statistical methods. In this paper, we present an ensemble clustering-based approach to develop prognostic systems of cancer patients. Our method starts with grouping combinations that are formed using levels of factors recorded in the data. The dissimilarity measure between combinations is obtained through a sequence of data
partitions produced by multiple use of PAM algorithm. This dissimilarity measure is then used with a hierarchical clustering method in order to find clusters of combinations. Prediction of survival is made simply by using the survival function derived from each cluster. Our approach admits multiple factors and provides a practical and useful tool in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients.
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Details
- Title
- Developing Prognostic Systems of Cancer Patients by Ensemble Clustering
- Creators
- Dechang Chen - Uniformed Services University of the Health SciencesKai Xing - Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA Department of Computer Science, The George Washington University, Washington DC 20052, USA The George Washington University Cancer Institute, The George Washington University, Washington DC 20037, USA Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA Department of Pathology, The George Washington University Medical Center, Washington DC 20037, USADonald Henson - Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA Department of Computer Science, The George Washington University, Washington DC 20052, USA The George Washington University Cancer Institute, The George Washington University, Washington DC 20037, USA Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA Department of Pathology, The George Washington University Medical Center, Washington DC 20037, USALi Sheng - Drexel UniversityArnold M. Schwartz - Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA Department of Computer Science, The George Washington University, Washington DC 20052, USA The George Washington University Cancer Institute, The George Washington University, Washington DC 20037, USA Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA Department of Pathology, The George Washington University Medical Center, Washington DC 20037, USAXiuzhen Cheng - Division of Epidemiology and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, MD 20814, USA Department of Computer Science, The George Washington University, Washington DC 20052, USA The George Washington University Cancer Institute, The George Washington University, Washington DC 20037, USA Department of Mathematics, Drexel University, Philadelphia, PA 19104, USA Department of Pathology, The George Washington University Medical Center, Washington DC 20037, USA
- Publication Details
- Journal of biomedicine & biotechnology, v 2009, pp 632786-7
- Publisher
- Hindawi Publishing Corporation
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000267551600001
- Scopus ID
- 2-s2.0-68149170955
- Other Identifier
- 991019168152104721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biotechnology & Applied Microbiology
- Medicine, Research & Experimental