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Diagnosis of ovarian cancer using decision tree classification of mass spectral data.

Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined with n-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns...

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Published in:Journal of Biomedicine & Biotechnology Vol. 2003; no. 5; pp. 308 - 315
Main Authors: Vlahou A, Schorge JO, Gregory BW, Coleman RL
Format: algorithm research tables/charts Journal Article
Published: Wiley-Blackwell 2003
Online Access:View this record in EBSCOhost
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      jid:
        11107243
        137K
      jtl: Journal of Biomedicine & Biotechnology
      issn: 11107243
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      dt: 2003
      vid: 2003
      iid: 5
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        106652438
        106652438
        2004166232
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        atl: Diagnosis of ovarian cancer using decision tree classification of mass spectral data.
      aug:
        au:
          Vlahou A
          Schorge JO
          Gregory BW
          Coleman RL
        affil: Dept of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA 23501; vlahoua@bioacademy.gr
      sug:
        subj:
          Ovarian Neoplasms Diagnosis
          Spectral Analysis
          Adult
          Aged
          Aged, 80 and Over
          Biological Markers
          Case Control Studies
          Female
          Middle Age
          Random Sample
          Sensitivity and Specificity
          Funding Source
          Human
          Adult: 19-44 years
          Aged: 65+ years
          Aged, 80 & over
          Middle Aged: 45-64 years
          Female
      ab: Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined with n-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns software (BPS), which is based on a classification and regression tree (CART), would be effective in discriminating ovarian cancer from benign diseases and healthy controls. Serum protein mass spectrum profiles from 139 patients with either ovarian cancer, benign pelvic diseases, or healthy women were analyzed using the BPS software. A decision tree, using five protein peaks, resulted in an accuracy of 81.5% in the cross-validation analysis and 80% in a blinded set of samples in differentiating the ovarian cancer from the control groups. The potential, advantages, and drawbacks of the BPS system as a. bioinformatic tool for the analysis of the SELDI high-dimensional proteomic data are discussed.
      pubtype: Academic Journal
      doctype:
        algorithm
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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