title: An Algorithm for Creating Prognostic Systems for Cancer.
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.
authors.affiliation: Department of Preventive Medicine and Biostatistics, The Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA. dechang.chen@usuhs.edu.
authors.forename: Dechang
authors.initials: D
authors.lastname: Chen
authors.affiliation: Department of Statistics, The George Washington University, Washington, DC, 20052, USA.
authors.forename: Huan
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Drexel University, Philadelphia, PA, 19104, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Sheng
authors.affiliation: Surgical Oncology, John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, 20889, USA.
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Hueman
authors.affiliation: Department of Preventive Medicine and Biostatistics, The Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA.|Department of Surgery, The Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, USA.
authors.forename: Donald E
authors.initials: DE
authors.lastname: Henson
authors.affiliation: Department of Pathology, The George Washington University Medical Center, Washington, DC, 20037, USA.|Department of Surgery, The George Washington University Medical Center, Washington, DC, 20037, USA.
authors.forename: Arnold M
authors.initials: AM
authors.lastname: Schwartz
authors.affiliation: Department of Surgery, Walter Reed National Military Medical Center, Bethesda, MD, 20889, USA.
authors.forename: Jigar A
authors.initials: JA
authors.lastname: Patel
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000401:mortality / Q000473:pathology*; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D009367:Neoplasm Staging; D011379:Prognosis; D018426:SEER Program; D047368:Tumor Burden
keywords: Area between curves; Breast cancer; Dendrogram; Hierarchical clustering; Prognostic system; Survival; TNM