An Algorithm for Creating Prognostic Systems for Cancer.
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...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 7; pp. 1 - 11 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115925378&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115925378 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2016 vid: 40 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115925378 115925378 115925378 10.1007/s10916-016-0518-1 115925378 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: An Algorithm for Creating Prognostic Systems for Cancer. aug: au: Chen, Dechang Wang, Huan Sheng, Li Hueman, Matthew Henson, Donald Schwartz, Arnold Patel, Jigar affil: Department of Preventive Medicine and Biostatistics, The Uniformed Services University of the Health Sciences, Bethesda 20814 USA sug: subj: Breast Neoplasms Prognosis Algorithms Human Female Neoplasm Grading Neoplasm Staging Kaplan-Meier Estimator Descriptive Statistics Funding Source Female ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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