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Swaroop S Singh

Publications and source records attributed to Swaroop S Singh.

3 recordsLinked to original sources

Java Web Start based software for automated quantitative nuclear analysis of prostate cancer and benign prostate hyperplasia.

BACKGROUND: Androgen acts via androgen receptor (AR) and accurate measurement of the levels of AR protein expression is critical for prostate research. The expression of AR in paired specimens of benign prostate and prostate cancer from 20 African and 20 Caucasian Americans was compared to demonstrate an application of this system. METHODS: A set of 200 immunopositive and 200 immunonegative nuclei were collected from the images using a macro developed in Image Pro Plus. Linear Discriminant and Logistic Regression analyses were performed on the data to generate classification coefficients. Classification coefficients render the automated image analysis software independent of the type of immunostaining or image acquisition system used. The image analysis software performs local segmentation and uses nuclear shape and size to detect prostatic epithelial nuclei. AR expression is described by (a) percentage of immunopositive nuclei; (b) percentage of immunopositive nuclear area; and (c) intensity of AR expression among immunopositive nuclei or areas. RESULTS: The percent positive nuclei and percent nuclear area were similar by race in both benign prostate hyperplasia and prostate cancer. In prostate cancer epithelial nuclei, African Americans exhibited 38% higher levels of AR immunostaining than Caucasian Americans (two sided Student's t-tests; P < 0.05). Intensity of AR immunostaining was similar between races in benign prostate. CONCLUSION: The differences measured in the intensity of AR expression in prostate cancer were consistent with previous studies. Classification coefficients are required due to non-standardized immunostaining and image collection methods across medical institutions and research laboratories and helps customize the software for the specimen under study. The availability of a free, automated system creates new opportunities for testing, evaluation and use of this image analysis system by many research groups who study nuclear protein expression.

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Sampling strategy for prostate tissue microarrays for Ki-67 and androgen receptor biomarkers.

OBJECTIVE: To develop an optimal sampling strategy for tissue microarrays using automated digital analysis for androgen receptor (heterogeneous expression) and the cellular proliferation marker Ki-67 (homogeneous expression and evaluated by others using nonautomated methods). STUDY DESIGN: Tissue microarrays were constructed from 23 radical prostatectomy specimens and immunostained for androgen receptor expression and cellular proliferation. Automated digital image analysis was used, and the minimum number of cores necessary to capture variance change <3% was determined. Androgen receptor immunostaining was described by percent positive nuclei (PPN) and mean optical density (MOD). RESULTS: Androgen receptor PPN variance measurements showed that 5 cores should be obtained when a single block of a radical prostatectomy specimen contained cancer. If all of 15 blocks contained cancer, 2 cores should be obtained from each of 6 blocks. An optimal sampling strategy was developed for androgen receptor PPN, androgen receptor MOD and Ki-67 PPN. CONCLUSION: The selection of the number of cores to sample is a tradeoff between the number of cores available that contain cancer and the amount of work involved in the analysis. Sampling no fewer than 5 but no more than 12 cores per radical prostatectomy specimen can capture tissue heterogeneity.

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