Our results demonstrate the existence of two specific patterns in the reactive microenvironment of FL, an immunosurveillance pattern (T lymphocytes and macrophages) and an immune-escape pattern (CD57+ T cells), that were directly associated with the clinicobiologic features of these patients.

Tissue microarray technology and immunohistochemical techniques have become a routine and indispensable tool for current anatomical pathology diagnosis. However, manual quantification by eye is relatively slow and subjective, and the use of digital image analysis software to extract information of immunostained specimens is an area of ongoing research, especially when the immunohistochemical signals have different localization in the cells (nuclear, membrane, cytoplasm). To minimize critical aspects of manual quantitative data acquisition, we generated semiautomated image-processing steps for the quantification of individual stained cells with immunohistochemical staining of different subcellular location. The precision of these macros was evaluated in 196 digital colour images of different Hodgkin lymphoma biopsies stained for different nuclear (Ki67, p53), cytoplasmic (TIA-1, CD68) and membrane markers (CD4, CD8, CD56, HLA-Dr). Semi-automated counts were compared to those obtained manually by three separate observers. Paired t -tests demonstrated significant differences between intra-and inter-observer measurements, with more substantial variability when the cellular density of the digital images was > 100 positive cells/image. Overall, variability was more pronounced for intra-observer than for inter-observer comparisons, especially for cytoplasmic and membrane staining patterns ( P < 0.0001 and P = 0.050). The comparison between the semi-automated and manual microscopic measurement methods indicates significantly lower variability in the results yielded by the former method. Our semi-automated computerized method eliminates the major causes of observer variability and may be considered a valid alternative to manual microscopic quantification for diagnostic, prognostic and therapeutic purposes.

Manual quantification of immunohistochemically stained nuclear markers is still laborious and subjective and the use of computerized systems for digital image analysis have not yet resolved the problems of nuclear clustering. In this study, we designed a new automatic procedure for quantifying various immunohistochemical nuclear markers with variable clustering complexity. This procedure consisted of two combined macros. The first, developed with a commercial software, enabled the analysis of the digital images using color and morphological segmentation including a masking process. All information extracted with this first macro was automatically exported to an Excel datasheet, where a second macro composed of four different algorithms analyzed all the information and calculated the definitive number of positive nuclei for each image. One hundred and eighteen images with different levels of clustering complexity was analyzed and compared with the manual quantification obtained by a trained observer. Statistical analysis indicated a great reliability (intra-class correlation coefficient > 0.950) and no significant differences between the two methods. Bland-Altman plot and Kaplan-Meier curves indicated that the results of both methods were concordant around 90% of analyzed images. In conclusion, this new automated procedure is an objective, faster and reproducible method that has an excellent level of accuracy, even with digital images with a high complexity.

Purpose: To analyze tumor-microenvironment relationships in Hodgkin lymphoma (HL) as potential determinants in the decision-making process related to the alterations in cell cycle and apoptotic pathways of Hodgkin/Reed-Sternberg (H/RS) cells. Experimental Design: Based on a cohort of 257 classic HL patients, we carried out a global descriptive correlational analysis and logistic regression study to identify tumor-infiltrated immune cell rate in HL that could be interconnected with genes involved in the regulation of apoptotic/proliferative pathways in H/RS cells. Results: Our results reveal the existence of a connection between the reactive microenvironment and molecular changes in apoptotic/proliferative pathways in H/RS cells. A lesser incidence of infiltrated cytotoxic cells in the tumor (CD8 + T lymphocytes, CD57 + natural killer, and granzyme B + cells) was associated with overexpression of antiapoptotic proteins (Bcl-X L , survivin, caspase-3, and nuclear factor-nB) in tumoral cells. Increased incidence of general infiltrated immune cells, such as CD4 + T lymphocytes, CD57 + natural killer cells, activated CTL, and dendritic cells, in the microenvironment of the tumor was associated with increased growth fraction of tumoral cells, including G 1 -S checkpoint (cyclin D and cyclin E) and tumor suppressor pathways (p16 and SKP2), and with the presence of EBV (signal transducers and activators of transcription 1and 3 expression; STAT1/STAT3). Conclusions: A lower level of cytotoxic cells correlated with an increase of antiapoptotic mechanisms in H/RS cells, whereas the global infiltrated immune population correlated with the growth fraction of the tumor. Our collective data suggest a causal relationship between infiltrated immune response and concurrent changes of the different proliferative checkpoints, tumor suppressor, and apoptotic pathways of H/RS cells in HL.

This study investigates the effects of digital image compression on automatic quantification of immunohistochemical nuclear markers. We examined 188 images with a previously validated computer-assisted analysis system. A first group was composed of 47 images captured in TIFF format, and other three contained the same images converted from TIFF to JPEG format with 3x, 23x and 46x compression. Counts of TIFF format images were compared with the other three groups. Overall, differences in the count of the images increased with the percentage of compression. Low-complexity images (< or =100 cells/field, without clusters or with small-area clusters) had small differences (<5 cells/field in 95-100% of cases) and high-complexity images showed substantial differences (<35-50 cells/field in 95-100% of cases). Compression does not compromise the accuracy of immunohistochemical nuclear marker counts obtained by computer-assisted analysis systems for digital images with low complexity and could be an efficient method for storing these images.

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The volume of digital image (DI) storage continues to be an important problem in computer-assisted pathology. DI compression enables the size of files to be reduced but with the disadvantage of loss of quality. Previous results indicated that the efficiency of computer-assisted quantification of immunohistochemically stained cell nuclei may be significantly reduced when compressed DIs are used. This study attempts to show, with respect to immunohistochemically stained nuclei, which morphometric parameters may be altered by the different levels of JPEG compression, and the implications of these alterations for automated nuclear counts, and further, develops a method for correcting this discrepancy in the nuclear count. For this purpose, 47 DIs from different tissues were captured in uncompressed TIFF format and converted to 1:3, 1:23 and 1:46 compression JPEG images. Sixty-five positive objects were selected from these images, and six morphological parameters were measured and compared for each object in TIFF images and those of the different compression levels using a set of previously developed and tested macros. Roundness proved to be the only morphological parameter that was significantly affected by image compression. Factors to correct the discrepancy in the roundness estimate were derived from linear regression models for each compression level, thereby eliminating the statistically significant differences between measurements in the equivalent images. These correction factors were incorporated in the automated macros, where they reduced the nuclear quantification differences arising from image compression. Our results demonstrate that it is possible to carry out unbiased automated immunohistochemical nuclear quantification in compressed DIs with a methodology that could be easily incorporated in different systems of digital image analysis.

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