An Algorithm for Creating Prognostic Systems for Cancer

Dechang Chen*, Huan Wang, Li Sheng, Matthew T. Hueman, Donald E. Henson, Arnold M. Schwartz, Jigar A. Patel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish
Article number160
JournalJournal of Medical Systems
Issue number7
StatePublished - 1 Jul 2016
Externally publishedYes


  • Area between curves
  • Breast cancer
  • Dendrogram
  • Hierarchical clustering
  • Prognostic system
  • Survival
  • TNM


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