Tissue architecture analysis in prostate cancer and its precursors: An innovative approach to computerized histometry.
OBJECTIVE
It is the aim of these studies to derive a numerically defined progression index for prostatic intraepithelial neoplasia (PIN) lesions.


METHODS
Histometric and karyometric features were automatically extracted from images of histopathologic sections by a machine vision system.


RESULTS
Both histometric and karyometric measures lend themselves to the defining of a progression index. Karyometric features were found to be more sensitive. They allow the detection of very early change.


CONCLUSIONS
It is possible to measure progression of PIN lesions with precision. The methodology would lend itself for measurement of regression due to chemopreventive intervention.
D001185:Artificial Intelligence; D002278:Carcinoma in Situ / Q000473:pathology*; D002467:Cell Nucleus / Q000473:pathology*; D003937:Diagnosis, Differential; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007620:Karyometry; D008297:Male; D011230:Precancerous Conditions / Q000473:pathology*; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000473:pathology*; D012016:Reference Values; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index