title: Classifying prostate cancer malignancy by quantitative histomorphometry.
abstract: Prostate cancer is routinely graded according to the Gleason grading scheme. This scheme is predominantly based on the textural appearance of aberrant glandular structures. Gleason grade is difficult to standardize and often leads to discussion due to interrater and intrarater disagreement. Thus, we investigated whether digital image based automated quantitative histomorphometry could be used to achieve a more standardized, reproducible classification outcome.
  
  
  
  In a proof of principle study we developed a method to evaluate digitized histological images of single prostate cancer regions in hematoxylin and eosin stained sections. Preprocessed color images were subjected to color deconvolution, followed by the binarization of obtained hematoxylin related image channels. Highlighted neoplastic epithelial gland related objects were morphometrically assessed by a classifier based on 2 calculated quantitative and objective geometric measures, that is inverse solidity and inverse compactness. The procedure was then applied to the prostate cancer probes of 125 patients. Each probe was independently classified for Gleason grade 3, 4 or 5 by an experienced pathologist blinded to image analysis outcome.
  
  
  
  Together inverse compactness and inverse solidity were adequate discriminatory features for a powerful classifier that distinguished Gleason grade 3 from grade 4/5 histology. The classifier was robust on sensitivity analysis.
  
  
  
  Results suggest that quantitative and interpretable measures can be obtained from image based analysis, permitting algorithmic differentiation of prostate Gleason grades. The method must be validated in a large independent series of specimens.
authors.affiliation: Institute for Medical Informatics, Statistics, and Epidemiology, University of Leipzig, Leipzig, Germany. loeffler@imise.uni-leipzig.de
authors.forename: Markus
authors.initials: M
authors.lastname: Loeffler
authors.forename: Lars
authors.initials: L
authors.lastname: Greulich
authors.forename: Patrick
authors.initials: P
authors.lastname: Scheibe
authors.forename: Philip
authors.initials: P
authors.lastname: Kahl
authors.forename: David
authors.initials: D
authors.lastname: Adler
authors.forename: Ulf-Dietrich
authors.initials: UD
authors.lastname: Braumann
authors.forename: Jens-Peer
authors.initials: JP
authors.lastname: Kuska
authors.forename: Nicolas
authors.initials: N
authors.lastname: Wernert
mesh_terms: D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D015999:Multivariate Analysis; D060787:Neoplasm Grading / Q000145:classification*; D011471:Prostatic Neoplasms / Q000473:pathology*