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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Acknowledgments
This work was partially supported by the grant Using Dendrograms to Create Prognostic Systems for Cancer sponsored by John P. Murtha Cancer Center Research Program. Arnold Schwartz was supported by the grant Prognostic Markers in Early Stage Lung Cancer: Computer Algorithms and Bayesian Regression?sponsored by the Dr. Cyrus and Myrtle Katzen Cancer Research Grant Award at The George Washington University. Disclaimer: The views expressed are those of the authors and do not necessarily reflect the official views of the Uniformed Services University of the Health Sciences, the Department of Defense, or the U.S. Government. Note: For those interested in the R code of the EACCD, contact the corresponding author.
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Chen, D., Wang, H., Sheng, L. et al. An Algorithm for Creating Prognostic Systems for Cancer. J Med Syst 40, 160 (2016). https://doi.org/10.1007/s10916-016-0518-1
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DOI: https://doi.org/10.1007/s10916-016-0518-1