Journal article
An Algorithm for Creating Prognostic Systems for Cancer
Journal of medical systems, Vol.40(7), pp.160-160
Jul 2016
PMID: 27189622
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The TNM staging system is universally used for classification of cancer. This system is limited since it uses only three factors (tumor size, extent of spread to lymph nodes, and status of distant metastasis) to generate stage groups. To provide a more accurate description of cancer and thus better patient care, additional factors or variables should be used to classify cancer. In this paper we propose a hierarchical clustering algorithm to develop prognostic systems that classify cancer according to multiple prognostic factors. This algorithm has many potential applications in augmenting the data currently obtained in a staging system by allowing more prognostic factors to be incorporated. The algorithm clusters combinations of prognostic factors that are formed using categories of factors. The dissimilarity between two combinations is determined by the area between two corresponding survival curves. Groups from cutting the dendrogram and survival curves of the individual groups define our prognostic systems that classify patients using survival outcomes. A demonstration of the proposed algorithm is given for patients with breast cancer from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute.
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Details
- Title
- An Algorithm for Creating Prognostic Systems for Cancer
- Creators
- Dechang Chen - Uniformed Services University of the Health SciencesHuan Wang - George Washington UniversityLi Sheng - Drexel UniversityMatthew T Hueman - Walter Reed National Military Medical CenterDonald E Henson - Uniformed Services University of the Health SciencesArnold M Schwartz - Washington University Medical CenterJigar A Patel - Walter Reed National Military Medical Center
- Publication Details
- Journal of medical systems, Vol.40(7), pp.160-160
- Publisher
- Springer Nature
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Identifiers
- 991019168060204721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Health Care Sciences & Services
- Medical Informatics