Clustering Cancer Data by Areas between Survival Curves

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a hierarchical clustering method for prognostic clustering of cancer patients. Dissimilarity between two subsets of patients is defined as the area between two corresponding Kaplan-Meier curves. The proposed method is applied to the breast cancer data from the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute and compared with the linkage approach. The proposed method is convenient to use and can generate dendrograms compatible with those from the linkage approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE 1st International Conference on Connected Health
Subtitle of host publicationApplications, Systems and Engineering Technologies, CHASE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages61-66
Number of pages6
ISBN (Electronic)9781509009435
DOIs
StatePublished - Aug 16 2016
Event1st IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016 - Washington, United States
Duration: Jun 27 2016Jun 29 2016

Publication series

NameProceedings - 2016 IEEE 1st International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016

Other

Other1st IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2016
Country/TerritoryUnited States
CityWashington
Period6/27/166/29/16

Keywords

  • TNM
  • area between curves
  • breast cancer
  • dendrogram
  • hierarchical clustering
  • prognostic system
  • survival

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management
  • Biomedical Engineering
  • Computer Networks and Communications
  • Hardware and Architecture

Fingerprint

Dive into the research topics of 'Clustering Cancer Data by Areas between Survival Curves'. Together they form a unique fingerprint.

Cite this