Search

The walls of angiogenic blood vessel capillaries are composed of two principal cell types, blood vessel endothelial cells ( BEC) and pericytes ( PC), whereas the walls of lymphatic capillaries are composed of lymphatic endothelial cells ( LEC). In this investigation we describe a practical application of NIH ImageJ software for quantitative image analysis for pericytes and endothelial cells in prostate cancer. We used a tissue microarray that contained 49 tissue cores (normal prostate tissue or prostatic carcinomas with Gleason scores of 6 through 10). These prostate cancer samples represented AJCC prognostic stages II, III, and IV. Slides were immunostained with anti- PDGFR-β antibody for identification of PC, and quantified as microvascular pericyte density ( MVPD); they were also immunostained with anti- CD34 antibody for identification of LEC and BEC simultaneously, and quantified as microvascular endothelial density ( MVED). CD31 and D2-40 immunostains were used to quantify BEC and lymphatic endothelial cells, respectively. Our results showed higher MVPD and MVED in prostate cancers with higher Gleason scores and higher stages, suggesting the prognostic utility of vascular image analysis in prostate pathology. This investigation demonstrates the feasibility, versatility, and ease of use of ImageJ software and pericyte-specific and endothelial-specific immunohistochemistry for quantitative image analysis in prostate pathology. [ABSTRACT FROM AUTHOR]

Abstract: Aims: Quantitative image analysis of histopathology slides is becoming an important technology in diagnostic pathology. To this end, it is essential to combine a robust image analysis software with the most commonly used immunohistochemical staining methods. In this investigation, we describe a practical application of NIH ImageJ software for quantitative vascular image analysis for diaminobenzene chromogen-based CD34 immunostain in breast cancer. CD34 immunostain is in a unique position to identify lymphangiogenesis and angiogenesis simultaneously in a given tumor tissue. This investigation aims at establishing a practical quantitative vascular image analysis solution for diagnostic pathologists by using ImageJ, and CD34 immunostain. Methods and results: Tissue microarray slides containing breast cancer tissue were immunostained for CD34 for simultaneous identification of lymphatic endothelial cells (LEC) and blood vessel endothelial cells (BEC). Digital images were analyzed using NIH ImageJ software. A CD34 score was quantified for each tissue core as a percentage (CD34-positive area/area of tissue core). The mean CD34 scores were 0.24%, 0.40%, 1.30%, 2.33%, 2.64%, and 3.44% for normal breast tissue, in stage IIA, IIB, IIIA, IIIB, and IIIC breast cancer tissue cores, respectively (p <0.0001). The mean CD34 scores were 0.70% and 2.21% for lymph node-negative and lymph node-positive breast cancer patients, respectively (p <0.0001). Conclusions: ImageJ software seems to be an attractive quantitative image analysis tool for diagnostic pathology for immunohistochemistry-based applications because of its capabilities, availability, and ease of use with most image formats. Our results show the feasibility, versatility, and ease of use of ImageJ and CD34 immunohistochemistry for vascular image analysis in breast pathology. Given the prospects of novel lymphatic and vascular endothelium-targeting therapeutics in breast oncology, the practical analysis of combined LEC and BEC density described in this report could enable diagnostic pathologists to apply quantitative vascular image analysis easily in their pathology practice and translational research. [Copyright &y& Elsevier]