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Journal of Biomedicine and Biotechnology
Volume 2003 (2003), Issue 5, Pages 308-314
Research article

Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data

1Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA 23501, USA
2Virginia Prostate Center, Eastern Virginia Medical School and Sentara Cancer Center, Norfolk, VA 23501, USA
3Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Texas Southwestern, Dallas, TX 75390, USA
4Foundation for Biomedical Research, Academy of Athens, Athens, Greece

Received 24 October 2002; Revised 16 February 2003; Accepted 19 February 2003

Copyright © 2003 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.