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An Algorithm for Creating Prognostic Systems for Cancer
Journal article   Peer reviewed

An Algorithm for Creating Prognostic Systems for Cancer

Dechang Chen, Huan Wang, Li Sheng, Matthew T Hueman, Donald E Henson, Arnold M Schwartz and Jigar A Patel
Journal of medical systems, v 40(7), pp 160-160
Jul 2016
PMID: 27189622

Abstract

Algorithms Breast Neoplasms - diagnosis Breast Neoplasms - mortality Breast Neoplasms - pathology Cluster Analysis Female Humans Kaplan-Meier Estimate Neoplasm Staging Prognosis SEER Program Tumor Burden
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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Collaboration types
Domestic collaboration
Web of Science research areas
Health Care Sciences & Services
Medical Informatics
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