title: Tissue Classification Using Optical Spectroscopy Accurately Differentiates Cancer and Chronic Pancreatitis.
abstract: Current pancreatic cancer diagnostics cannot reliably detect early disease or distinguish it from chronic pancreatitis. We test the hypothesis that optical spectroscopy can accurately differentiate cancer from chronic pancreatitis and normal pancreas. We developed and tested clinically compatible multimodal optical spectroscopy technology to measure reflectance and endogenous fluorescence from human pancreatic tissues.
  
  
  
  Freshly excised pancreatic tissue specimens (39 normal, 34 chronic pancreatitis, 32 adenocarcinoma) from 18 patients were optically interrogated, with site-specific histopathology representing the criterion standard. A multinomial logistic model using principal component analysis and generalized estimating equations provided statistically rigorous tissue classification.
  
  
  
  Optical spectroscopy distinguished pancreatic cancer from normal pancreas and chronic pancreatitis (sensitivity, 91%; specificity, 82%; positive predictive value, 69%; negative predictive value, 95%; area under receiver operating characteristic curve, 0.89). Reflectance alone provided essentially the same classification accuracy as reflectance and fluorescence combined, suggesting that a rapid, low-cost, reduced-footprint, reflectance-based device could be deployed without notable loss of diagnostic power.
  
  
  
  Our novel, clinically compatible, label-free optical diagnostic technology accurately characterizes pancreatic tissues. These data provide the scientific foundation demonstrating that optical spectroscopy can potentially improve diagnosis of pancreatic cancer and chronic pancreatitis.
authors.affiliation: From the *Department of Biomedical Engineering, †Department of Internal Medicine, ‡Comprehensive Cancer Center §Department of Biostatistics, ∥Department of Pathology, and ¶Department of Surgery, University of Michigan, Ann Arbor, MI.
authors.forename: Robert H
authors.initials: RH
authors.lastname: Wilson
authors.forename: Malavika
authors.initials: M
authors.lastname: Chandra
authors.forename: James M
authors.initials: JM
authors.lastname: Scheiman
authors.forename: Seung Yup
authors.initials: SY
authors.lastname: Lee
authors.forename: Oliver E
authors.initials: OE
authors.lastname: Lee
authors.forename: Barbara J
authors.initials: BJ
authors.lastname: McKenna
authors.forename: Diane M
authors.initials: DM
authors.lastname: Simeone
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
authors.forename: Mary-Ann
authors.initials: MA
authors.lastname: Mycek
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis*; D003937:Diagnosis, Differential; D006801:Humans; D010179:Pancreas / Q000473:pathology; D010190:Pancreatic Neoplasms / Q000175:diagnosis*; D050500:Pancreatitis, Chronic / Q000175:diagnosis*; D025341:Principal Component Analysis; D012372:ROC Curve; D015203:Reproducibility of Results; D013057:Spectrum Analysis / Q000295:instrumentation / Q000379:methods*