Histometric features for the grading of prostatic carcinoma.
Histometric features for the objective grading of prostatic adenocarcinoma in histologic specimens were analyzed in five cases each of well, moderately and poorly differentiated lesions. Tissue sections from the selected cases were stained by the Feulgen method and digitized by a video-based microphotometer. Twenty total fields were recorded for each grade: ten at high resolution (an image sampling of 0.5 micron per pixel) and ten at low resolution (0.8 micron per pixel), with two fields per case recorded at each resolution. The images were segmented by an automated expert system-guided scene segmentation procedure. The performance of that procedure was measured by comparing the automated counts of nuclei in the segmented fields to the visual counts made by a pathologist in the same fields. For well, moderately and poorly differentiated cases, respectively, the nuclear counts made by the expert system at high resolution were 2.7%, 4.2% and 4.7% higher than the visual counts (as estimated from a total of 6,628 nuclei), but 1.2%, 2.5% and 1.1% lower at low resolution (10,329 nuclei). High-resolution features and tissue textural features were computed for each case. The high-resolution features showed good separation between the three groups of cases. The tissue textural features showed consistent separation between well and moderately differentiated cases. The relaxation of the spatial resolution (to 0.8 micron/pixel spacing) did not affect the selection of features, but led to less separation between the data from different grades. In conclusion, the automated system performed satisfactorily in distinguishing sections of prostatic tumors of varying degrees of differentiation.
Bibbo
M
M
Department of Pathology, University of Chicago, Illinois 60637.
Kim
D H
DH
Pfeifer
T
T
Dytch
H E
HE
Galera-Davidson
H
H
Bartels
P H
PH
D000230:Adenocarcinoma / Q000473:pathology* / Q000648:ultrastructure; D000704:Analysis of Variance; D002467:Cell Nucleus / Q000473:pathology; D005103:Expert Systems; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D011471:Prostatic Neoplasms / Q000473:pathology* / Q000648:ultrastructure