Classifying prostate cancer malignancy by quantitative histomorphometry.


PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

PubMed ID: 22424674

Projects: ProstataCA

Publication type: Not specified

Journal: J Urol

Human Diseases: Prostate cancer

Citation: J Urol. 2012 May;187(5):1867-75. doi: 10.1016/j.juro.2011.12.054. Epub 2012 Mar 16.

Date Published: 20th Mar 2012

Registered Mode: by PubMed ID

Authors: M. Loeffler, L. Greulich, P. Scheibe, P. Kahl, Z. Shaikhibrahim, U. D. Braumann, J. P. Kuska, N. Wernert

help Submitter

Views: 2670

Created: 29th Aug 2019 at 11:36

Last updated: 7th Dec 2021 at 17:58

help Tags

This item has not yet been tagged.

help Attributions


Related items

Powered by
Copyright © 2008 - 2021 The University of Manchester and HITS gGmbH
Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig

By continuing to use this site you agree to the use of cookies