title: Rapid mass spectrometric metabolic profiling of blood sera detects ovarian cancer with high accuracy.
abstract: BACKGROUND
  Ovarian cancer diagnosis is problematic because the disease is typically asymptomatic, especially at the early stages of progression and/or recurrence. We report here the integration of a new mass spectrometric technology with a novel support vector machine computational method for use in cancer diagnostics, and describe the application of the method to ovarian cancer.
  
  
  METHODS
  We coupled a high-throughput ambient ionization technique for mass spectrometry (direct analysis in real-time mass spectrometry) to profile relative metabolite levels in sera from 44 women diagnosed with serous papillary ovarian cancer (stages I-IV) and 50 healthy women or women with benign conditions. The profiles were input to a customized functional support vector machine-based machine-learning algorithm for diagnostic classification. Performance was evaluated through a 64-30 split validation test and with a stringent series of leave-one-out cross-validations.
  
  
  RESULTS
  The assay distinguished between the cancer and control groups with an unprecedented 99% to 100% accuracy (100% sensitivity and 100% specificity by the 64-30 split validation test; 100% sensitivity and 98% specificity by leave-one-out cross-validations).
  
  
  CONCLUSIONS
  The method has significant clinical potential as a cancer diagnostic tool. Because of the extremely low prevalence of ovarian cancer in the general population (approximately 0.04%), extensive prospective testing will be required to evaluate the test's potential utility in general screening applications. However, more immediate applications might be as a diagnostic tool in higher-risk groups or to monitor cancer recurrence after therapeutic treatment.
  
  
  CONCLUSIONS
  The ability to accurately and inexpensively diagnose ovarian cancer will have a significant positive effect on ovarian cancer treatment and outcome.
authors.affiliation: Georgia Institute of Technology, Atlanta, Georgia 30332-0363, USA.
authors.forename: Manshui
authors.initials: M
authors.lastname: Zhou
authors.forename: Wei
authors.initials: W
authors.lastname: Guan
authors.forename: L DeEtte
authors.initials: LD
authors.lastname: Walker
authors.forename: Roman
authors.initials: R
authors.lastname: Mezencev
authors.forename: Benedict B
authors.initials: BB
authors.lastname: Benigno
authors.forename: Alexander
authors.initials: A
authors.lastname: Gray
authors.forename: Facundo M
authors.initials: FM
authors.lastname: Fernández
authors.forename: John F
authors.initials: JF
authors.lastname: McDonald
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D055442:Metabolome; D008875:Middle Aged; D010051:Ovarian Neoplasms / Q000097:blood* / Q000175:diagnosis / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor