• DocumentCode
    2251910
  • Title

    Biomarker clustering of colorectal cancer data to complement clinical classification

  • Author

    Roadknight, Christopher ; Aickelin, Uwe ; Ladas, Alexandras ; Soria, Daniele ; Scholefield, John ; Durrant, Lindy

  • fYear
    2012
  • fDate
    9-12 Sept. 2012
  • Firstpage
    187
  • Lastpage
    191
  • Abstract
    In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to cluster this dataset and important subsets of it in an effort to characterize the data and validate existing standards for tumour classification. It is apparent from optimal clustering that existing tumour classification is largely unrelated to immunological factors within a patient and that there may be scope for re-evaluating treatment options and survival estimates based on a combination of tumour physiology and patient histochemistry.
  • Keywords
    cancer; medical computing; pattern classification; pattern clustering; tumours; and patient histochemistry; biomarker clustering; clinical classification; colorectal cancer data; colorectal tumours; immunological status; optimal clustering; treatment options; tumour classification; tumour physiology; tumour removal; Cancer; Educational institutions; Immune system; Indexes; Measurement; Tumors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on
  • Conference_Location
    Wroclaw
  • Print_ISBN
    978-1-4673-0708-6
  • Electronic_ISBN
    978-83-60810-51-4
  • Type

    conf

  • Filename
    6354464