OBJECTIVE: To examine the ability of quantitative histomorphometry to predict DNA ploidy of prostate carcinoma in biopsy tissue sections assigned after quantitation by nuclear digital image analysis. STUDY DESIGN: Thirty-five diploid, 35 tetraploid and 35 aneuploid prostatic carcinomas in biopsies, assessed by the CAS 200 image analyzer (Bacus Laboratories, Lombard, Illinois, U.S.A.), were reevaluated by the Bacus Laboratories Incorporated Slide Scanner, a microscope that quantifies histologic images. Thirty-one histomorphometric features from cancer cells were captured at 40 x magnification, averaged across tilesfor each case and incorporated into a multivariate discriminant model to determine which features predicted ploidy interpretation by nuclear image analysis using the CAS 200. RESULTS: On average, 60 and 15 minutes were required to perform nuclear image analysis and histomorphometry, respectively. The multivariate discriminant model identified configurable run length, difference variance, contrast, inverse difference moment, sum entropy and diagonal variance as histomorphometry features capable of distinguishing diploid from nondiploid tumors (P < .05). Cross-validation studies showed the model correctly classified 74.3% of the diploid and 57.1% of the nondiploid cases. CONCLUSION: Quantitative histomorphometry can predict the ploidy of prostate carcinoma in biopsy tissue sections. Quantitative histomorphometry has potential as a method of rapidly assessing DNA ploidy otherwise earmarked for nuclear image analysis, resulting in savings of time and expense.
OBJECTIVE: To examine the ability of quantitative histomorphometry to predict DNA ploidy of prostate carcinoma in biopsy tissue sections assigned after quantitation by nuclear digital image analysis. STUDY DESIGN: Thirty-five diploid, 35 tetraploid and 35 aneuploid prostatic carcinomas in biopsies, assessed by the CAS 200 image analyzer (Bacus Laboratories, Lombard, Illinois, U.S.A.), were reevaluated by the Bacus Laboratories Incorporated Slide Scanner, a microscope that quantifies histologic images. Thirty-one histomorphometric features from cancer cells were captured at 40 x magnification, averaged across tilesfor each case and incorporated into a multivariate discriminant model to determine which features predicted ploidy interpretation by nuclear image analysis using the CAS 200. RESULTS: On average, 60 and 15 minutes were required to perform nuclear image analysis and histomorphometry, respectively. The multivariate discriminant model identified configurable run length, difference variance, contrast, inverse difference moment, sum entropy and diagonal variance as histomorphometry features capable of distinguishing diploid from nondiploid tumors (P < .05). Cross-validation studies showed the model correctly classified 74.3% of the diploid and 57.1% of the nondiploid cases. CONCLUSION: Quantitative histomorphometry can predict the ploidy of prostate carcinoma in biopsy tissue sections. Quantitative histomorphometry has potential as a method of rapidly assessing DNA ploidy otherwise earmarked for nuclear image analysis, resulting in savings of time and expense.
Authors: Girish Venkataraman; Vijayalakshmi Ananthanarayanan; Gladell P Paner; Rui He; Saeedeh Masoom; James Sinacore; Robert C Flanigan; Eva M Wojcik Journal: Virchows Arch Date: 2006-08-03 Impact factor: 4.064