title: Ability to predict metastasis based on pathology findings and alterations in nuclear structure of normal-appearing and cancer peripheral zone epithelium in the prostate.
abstract: OBJECTIVE
  Malignant transformation in the prostate produces significant alterations in glandular architecture (Gleason grade) and nuclear structure that provide valuable prognostic information. Normal-appearing nuclei (NN) adjacent to cancer may also have altered functions in response to malignancy. We studied NN adjacent to peripheral zone (PZ) prostate cancer (PCa), as well as the PZ cancer nuclei (CaN) using quantitative image cytometry. The nuclear structure information was combined with routine pathological findings to predict metastatic PCa progression and/or death.
  
  
  METHODS
  Tissue microarrays of normal-appearing and cancer areas were prepared from 182 pathologist-selected paraffin blocks. Feulgen-stained CaN and NN were captured from the tissue microarrays using the AutoCyte Pathology Workstation. Multivariate logistic regression was used to calculate quantitative nuclear grade (QNG) solutions based on nuclear morphometric descriptors determined from NN and CaN. Multivariate logistic regression and Kaplan-Meier plots were also used to predict risk for distant metastasis and/or PCa-specific death using QNG solutions and routine pathology.
  
  
  RESULTS
  The pathology model yielded an area under the receiver operator characteristic curve of 72.5%. The QNG-NN and QNG-CaN solutions yielded an area under the receiver operator characteristic curve of 81.6 and 79.9%, respectively, but used different sets of nuclear morphometric descriptors. Kaplan-Meier plots for the pathology variables, the QNG-NN and QNG-CaN solutions, were combined with pathology to defined three statistically significantly distinct risk groups for distant metastasis and/or death (P < 0.0001).
  
  
  CONCLUSIONS
  Alterations in cancer or normal-appearing nuclei adjacent to peripheral zone cancer areas can predict PCa progression and/or death. The QNG-NN and QNG-CA solutions could be combined with pathology variables to improve the prediction of distant metastasis.
authors.affiliation: The James Buchanan Brady Urological Institute, The Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA. rveltri1@jhmi.edu
authors.forename: Robert W
authors.initials: RW
authors.lastname: Veltri
authors.forename: Masood A
authors.initials: MA
authors.lastname: Khan
authors.forename: M Craig
authors.initials: MC
authors.lastname: Miller
authors.forename: Jonathan I
authors.initials: JI
authors.lastname: Epstein
authors.forename: Leslie A
authors.initials: LA
authors.lastname: Mangold
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Walsh
authors.forename: Alan W
authors.initials: AW
authors.lastname: Partin
mesh_terms: D000328:Adult; D000368:Aged; D002467:Cell Nucleus / Q000378:metabolism* / Q000648:ultrastructure*; D018450:Disease Progression; D004848:Epithelium / Q000473:pathology; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009362:Neoplasm Metastasis; D020411:Oligonucleotide Array Sequence Analysis; D011467:Prostate / Q000473:pathology*; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000401:mortality / Q000473:pathology* / Q000601:surgery; D012372:ROC Curve; D012008:Recurrence; D013997:Time Factors; D016896:Treatment Outcome