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

5α-reductase type 3 expression in human benign and malignant tissues: a comparative analysis during prostate cancer progression

BACKGROUND

A third isozyme of human 5α-steroid reductase, 5α-reductase-3, was identified in prostate tissue at the mRNA level. However, the levels of 5α-reductase-3 protein expression and its cellular localization in human tissues remain unknown.

METHODS

A specific monoclonal antibody was developed, validated, and used to characterize for the first time the expression of 5α-reductase-3 protein in 18 benign and 26 malignant human tissue types using immunostaining analyses.

RESULTS AND CONCLUSIONS

In benign tissues, 5α-reductase-3 immunostaining was high in conventional androgen-regulated human tissues, such as skeletal muscle and prostate. However, high levels of expression also were observed in non-conventional androgen-regulated tissues, which suggest either multiples target tissues for androgens or different functions of 5α-reductase-3 among human tissues. In malignant tissues, 5α-reductase-3 immunostaining was ubiquitous but particularly over-expressed in some cancers compared to their benign counterparts, which suggests a potential role for 5α-reductase-3 as a biomarker of malignancy. In benign prostate, 5α-reductase-3 immunostaining was localized to basal epithelial cells, with no immunostaining observed in secretory/luminal epithelial cells. In high-grade prostatic intraepithelial neoplasia (HGPIN), 5α-reductase-3 immunostaining was localized in both basal epithelial cells and neoplastic epithelial cells characteristic of HGPIN. In androgen-stimulated and castration-recurrent prostate cancer (CaP), 5α-reductase-3 immunostaining was present in most epithelial cells and at similar levels, and at levels higher than observed in benign prostate. Analyses of expression and functionality of 5α-reductase-3 in human tissues may prove useful for development of treatment for benign prostatic enlargement and prevention and treatment of CaP.

Second to fourth digit ratio: a predictor of prostate-specific antigen level and the presence of prostate cancer

OBJECTIVE

To investigate the relationships between the 2nd to 4th digit ratio (digit ratio) and prostate volume, prostate-specific antigen (PSA) level, and the presence of prostate cancer.

PATIENTS AND METHODS

Of the men that presented with lower urinary tract symptoms (LUTS) at a single tertiary academic center, 366 men aged 40 or older with a PSA level ≤ 40 ng/mL were prospectively enrolled. Right-hand 2nd and 4th digit lengths were measured prior to the PSA determinations and transrectal ultrasonography (TRUS). Prostate volumes were measured by TRUS without information about digit length. Patients with a PSA level ≥ 3 ng/mL underwent prostate biopsy.

RESULTS

No relationship was found between prostate volume and digit ratio [correlation coefficient (r) = -0.038, P = 0.466]. But, significant negative correlations were found between digit ratio and PSA (r = -0.140, P = 0.007). When the patients were divided into two groups (Group A: digit ratio < 0.95, n = 184; Group B: digit ratio ≥ 0.95, n = 182), Group A had a higher mean PSA level than Group B (3.26 ± 5.54 ng/mL vs 1.89 ± 2.24 ng/mL, P = 0.002) and had significantly higher risks of prostate biopsy [odds ratio (OR) = 1.75, 95% CI = 1.07-2.84] and prostate cancer (OR = 3.22, 95% CI = 1.33-7.78).

CONCLUSIONS

Patients with a lower digit ratio have higher risks of prostate biopsy and prostate cancer.

An optimal transportation approach for nuclear structure-based pathology

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g., normal versus cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers.

JPEG2000 for automated quantification of immunohistochemically stained cell nuclei: a comparative study with standard JPEG format

The Joint Photographic Experts Group (JPEG) standard format is one of the most widely used in image compression technologies. More recently, JPEG2000 format has emerged as a state-of-the-art technology that provides substantial improvements in picture quality at higher compression ratios. However, there has been no attempt to date to determine which of the two compression formats produces less variability in the automated evaluation of immunohistochemically stained digital images in agreement with their compression rates and complexity degrees. The evaluation of Ki67 and FOXP3 immunohistochemical nuclear markers was performed in a total of 329 digital images: 47 were captured in uncompressed Tagged Image File Format (TIFF), 141 were converted to three JPEG compressed formats (47 each with 1:3, 1:23 and 1:46 compression) and 141 were converted to three JPEG2000 compressed formats (47 each with 1:3, 1:23 and 1:46 compression). The count differences between images in TIFF versus JPEG formats were compared with those obtained between images in TIFF versus JPEG2000 formats at the three levels of compression. It was found that, using JPEG2000 compression, the results of the stained nuclei count are close enough to the results obtained with uncompressed images, especially in highly complex images at minimum and medium compression. Otherwise, in images of low complexity, JPEG and JPEG2000 had similar count efficiency to that of the original TIFF images at all compression levels. These data suggest that JPEG2000 could give rise to an efficient means of storage, reducing file size and storage capacity, without compromise on the immunohistochemical analytical quality.

Detection and classification of thyroid follicular lesions based on nuclear structure from histopathology images

Follicular lesions of the thyroid are traditionally difficult and tedious challenges in diagnostic surgical pathology in part due to lack of obvious discriminatory cytological and microarchitectural features. We describe a computerized method to detect and classify follicular adenoma of the thyroid, follicular carcinoma of the thyroid, and normal thyroid based on the nuclear chromatin distribution from digital images of tissue obtained by routine histological methods. Our method is based on determining whether a set of nuclei, obtained from histological images using automated image segmentation, is most similar to sets of nuclei obtained from normal or diseased tissues. This comparison is performed utilizing numerical features, a support vector machine, and a simple voting strategy. We also describe novel methods to identify unique and defining chromatin patterns pertaining to each class. Unlike previous attempts in detecting and classifying these thyroid lesions using computational imaging, our results show that our method can automatically classify the data pertaining to 10 different human cases with 100% accuracy after blind cross validation using at most 43 nuclei randomly selected from each patient. We conclude that nuclear structure alone contains enough information to automatically classify the normal thyroid, follicular carcinoma, and follicular adenoma, as long as groups of nuclei (instead of individual ones) are used. We also conclude that the distribution of nuclear size and chromatin concentration (how tightly packed it is) seem to be discriminating features between nuclei of follicular adenoma, follicular carcinoma, and normal thyroid.

Comparison of ACINUS, caspase-3, and TUNEL as apoptotic markers in determination of tumor growth rates of clinically localized prostate cancer using image analysis

BACKGROUND

The balance between apoptotic and proliferative processes determines the enlargement of a tumor. Accurate measurement of apoptotic and proliferative rates from diagnostic prostate biopsies would allow calculation of tumor growth rates in a population-based prostate cancer (CaP) study. Automated image analysis may be used if proliferation and apoptotic biomarkers provide clearly resolved immunostained images.

METHODS

Clinical CaP aggressiveness was assigned as low, intermediate or high using clinical criteria for 46 research subjects with newly diagnosed CaP. Diagnostic biopsy sections from the research subjects were dual-labeled for proliferation biomarker, Ki-67 and apoptotic biomarker, apoptotic chromatin condensation inducer in the nucleus (ACINUS). Apoptotic biomarkers, caspase-3 and terminal deoxyribonucleotidyltransferase mediated dUTP-biotin nick end labeling (TUNEL) were labeled separately. Images from immunostained sections were analyzed using automated image analysis and tumor growth rates computed. Association between clinical CaP aggressiveness and tumor growth rates was explored.

RESULTS

Sixteen subjects had high, 17 had intermediate, and 13 had low clinical CaP aggressiveness. Positive immunostaining was localized to the nucleus for Ki-67, ACINUS, and TUNEL. A statistically significant linear trend across clinical CaP aggressiveness categories was found when tumor growth rates were calculated using ACINUS (P = 0.046). Logistic regression and ROC plots generated showed ACINUS (AUC = 0.677, P = 0.048) and caspase-3 (AUC = 0.694, P = 0.038) to be better predictors than TUNEL (AUC = 0.669, P = 0.110).

CONCLUSIONS

ACINUS met the criteria for automated image analysis and for calculation of apoptotic rate. Tumor growth rates determined using automated image analysis should be evaluated for clinical prediction of CaP aggressiveness, treatment response, recurrence, and mortality.

Roundness variation in JPEG images affects the automated process of nuclear immunohistochemical quantification: correction with a linear regression model

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.

Effects of image compression on automatic count of immunohistochemically stained nuclei in digital images

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.

Quantification of diverse subcellular immunohistochemical markers with clinicobiological relevancies: validation of a new computer-assisted image analysis procedure

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 semi-automated 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.

Automated recognition of cell phenotypes in histology images based on membrane- and nuclei-targeting biomarkers

Background

Three-dimensional in vitro culture of cancer cells are used to predict the effects of prospective anti-cancer drugs in vivo. In this study, we present an automated image analysis protocol for detailed morphological protein marker profiling of tumoroid cross section images.

Methods

Histologic cross sections of breast tumoroids developed in co-culture suspensions of breast cancer cell lines, stained for E-cadherin and progesterone receptor, were digitized and pixels in these images were classified into five categories using k-means clustering. Automated segmentation was used to identify image regions composed of cells expressing a given biomarker. Synthesized images were created to check the accuracy of the image processing system.

Results

Accuracy of automated segmentation was over 95% in identifying regions of interest in synthesized images. Image analysis of adjacent histology slides stained, respectively, for Ecad and PR, accurately predicted regions of different cell phenotypes. Image analysis of tumoroid cross sections from different tumoroids obtained under the same co-culture conditions indicated the variation of cellular composition from one tumoroid to another. Variations in the compositions of cross sections obtained from the same tumoroid were established by parallel analysis of Ecad and PR-stained cross section images.

Conclusion

Proposed image analysis methods offer standardized high throughput profiling of molecular anatomy of tumoroids based on both membrane and nuclei markers that is suitable to rapid large scale investigations of anti-cancer compounds for drug development.