title: Pathology of Barrett's esophagus by proton magnetic resonance spectroscopy and a statistical classification strategy.
abstract: BACKGROUND
  Barrett's esophagus is thought to be a precursor of adenocarcinoma. The incidence of adenocarcinoma of the lower esophagus in the Western world is rising and accounts for more than 40% of esophageal carcinomas in males. It is not possible to identify which Barrett's patients are at high risk of developing malignancy. Here we applied a statistical classification strategy to the analysis of magnetic resonance spectroscopy and histopathological data from esophageal biopsies to ascertain whether this risk could be identified in Barrett's patients.
  
  
  METHODS
  Tissue specimens from 72 patients (29 noncancer-bearing and 43 cancer-bearing) were analyzed by one-dimensional proton magnetic resonance spectroscopy at 8.5 Tesla. Diagnostic correlation was performed between the magnetic resonance spectra and histopathology. The magnetic resonance magnitude spectra were preprocessed, followed by identification of optimal spectral regions, and were then classified by cross-validated linear discriminant analysis of rank orders of the first derivative of magnetic resonance spectra.
  
  
  RESULTS
  Magnetic resonance spectroscopy combined with a statistical classification strategy analysis distinguished normal esophagus from adenocarcinoma and Barrett's epithelium with an accuracy of 100%. Barrett's epithelium and adenocarcinoma were distinguished with an accuracy of 98.6% but only when 4 of the Barrett's specimens and 7 of the carcinoma specimens, determined to be "fuzzy" (ie, unable to be accurately assigned to either class) were withdrawn. The 7 cancer and 4 Barrett's specimens, determined to be "fuzzy" using the Barrett's versus cancer (B versus C) classifier, were submitted to the other two classifiers (Barrett's versus normal [B versus N] and normal versus cancer [N versus C], respectively). The 4 Barrett's specimens were assigned to Barrett's by the N versus B classifier and to normal (n = 2) or cancer (n = 2) classes by the N versus C classifier. The 7 cancer specimens were crisply assigned to the cancer class (N versus C), or for the B versus N classifier, to the Barrett's class (ie, more similar to Barrett's than to normal tissue). Visual inspection of the spectra from histologically identified Barrett's epithelium showed a gradation from normal to carcinoma.
  
  
  CONCLUSIONS
  Proton magnetic resonance spectroscopy of esophageal biopsies combined with a statistical classification strategy data analysis provides a robust diagnosis with a high degree of accuracy for discriminating normal epithelium from esophageal adenocarcinoma and Barrett's esophagus. Different spectral categories of Barrett's epithelium were identified both by visual inspection and by statistical classification strategy, possibly reflecting the risk of future malignant transformation.
authors.affiliation: Institute for Magnetic Resonance Research and Department of Magnetic Resonance in Medicine, University of Sydney, P.O. Box 148, St. Leonards, New South Wales 1590, Australia.
authors.forename: Sinead T
authors.initials: ST
authors.lastname: Doran
authors.forename: Greg L
authors.initials: GL
authors.lastname: Falk
authors.forename: Ray L
authors.initials: RL
authors.lastname: Somorjai
authors.forename: Cynthia L
authors.initials: CL
authors.lastname: Lean
authors.forename: Uwe
authors.initials: U
authors.lastname: Himmelreich
authors.forename: Jeanette
authors.initials: J
authors.lastname: Philips
authors.forename: Peter
authors.initials: P
authors.lastname: Russell
authors.forename: Brion
authors.initials: B
authors.lastname: Dolenko
authors.forename: Alexander E
authors.initials: AE
authors.lastname: Nikulin
authors.forename: Carolyn E
authors.initials: CE
authors.lastname: Mountford
mesh_terms: D000230:Adenocarcinoma / Q000737:chemistry / Q000175:diagnosis / Q000473:pathology; D001471:Barrett Esophagus / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D004848:Epithelium / Q000737:chemistry / Q000473:pathology; D004938:Esophageal Neoplasms / Q000737:chemistry / Q000175:diagnosis / Q000473:pathology; D004947:Esophagus / Q000033:anatomy & histology / Q000737:chemistry; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D009716:Numerical Analysis, Computer-Assisted; D012680:Sensitivity and Specificity