Technical note on the validation of a semi-automated image analysis software application for estrogen and progesterone receptor detection in breast cancer.

BACKGROUND: The immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors in breast cancer is routinely used for prognostic and predictive testing. Whole slide digitalization supported by dedicated software tools allows quantization of the image objects (e.g. cell membrane, nuclei) and an unbiased analysis of immunostaining results. Validation studies of image analysis applications for the detection of ER and PR in breast cancer specimens provided strong concordance between the pathologist's manual assessment of slides and scoring performed using different software applications. METHODS: The effectiveness of two connected semi-automated image analysis software (NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14) for determination of ER and PR status in formalin-fixed paraffin embedded breast cancer specimens immunostained with the automated Leica Bond Max system was studied. First the detection algorithm was calibrated to the scores provided an independent assessors (pathologist), using selected areas from 38 small digital slides (created from 16 cases) containing a mean number of 195 cells. Each cell was manually marked and scored according to the Allred-system combining frequency and intensity scores. The performance of the calibrated algorithm was tested on 16 cases (14 invasive ductal carcinoma, 2 invasive lobular carcinoma) against the pathologist's manual scoring of digital slides. RESULTS: The detection was calibrated to 87 percent object detection agreement and almost perfect Total Score agreement (Cohen's kappa 0.859, quadratic weighted kappa 0.986) from slight or moderate agreement at the start of the study, using the un-calibrated algorithm. The performance of the application was tested against the pathologist's manual scoring of digital slides on 53 regions of interest of 16 ER and PR slides covering all positivity ranges, and the quadratic weighted kappa provided almost perfect agreement (κ = 0.981) among the two scoring schemes. CONCLUSIONS: NuclearQuant v. 1.13 application for Pannoramic™ Viewer v. 1.14 software application proved to be a reliable image analysis tool for pathologists testing ER and PR status in breast cancer.

Digital microscopy for boosting database integration and analysis in TMA studies.

The enormous amount of clinical, pathological, and staining data to be linked, analyzed, and correlated in a tissue microarray (TMA) project makes digital slides ideal to be integrated into TMA database systems. With the help of a computer and dedicated software tools, digital slides offer dynamic access to microscopic information at any magnification with easy navigation, annotation, measurement, and archiving features. Advanced slide scanners work both in transmitted light and fluorescent modes to support biomarker testing with immunohistochemistry, immunofluorescence or fluorescence in situ hybridization (FISH). Currently, computer-driven integrated systems are available for creating TMAs, digitalizing TMA slides, linking sample and staining data, and analyzing their results. Digital signals permit image segmentation along color, intensity, and size for automated object quantification where digital slides offer superior imaging features and batch processing. In this chapter, the workflow and the advantages of digital TMA projects are demonstrated through the project-based MIRAX system developed by 3DHISTECH and supported by Zeiss.The enhanced features of digital slides compared with those of still images can boost integration and intelligence in TMA database management systems, offering essential support for high-throughput biomarker testing, for example, in tumor progression/prognosis, drug discovery, and target therapy research.

Automated multichannel fluorescent whole slide imaging and its application for cytometry.

Slide-based image cytometry (SBC) has several advantages over flow cytometry but it is not widely used because of its low throughput, complicated workflow, and high price. Fully automated microscopes became affordable with the advent of whole slide imaging (WSI) and they can be transformed into a cytometer. A MIRAX MIDI automated whole slide imager was used with metal-halide and light emitting diode (LED)-based fluorescent illumination, filter block changer, and a cooled monochrome charge coupled device camera. The MIRAX control software was further developed for fluorescent sample detection, autofocusing, multichannel digitization, and signal correction due to nonuniform illumination. Fluorescent calibration beads were used to verify the linearity of the system. The HistoQuant software package of the MIRAX viewer was used for image segmentation and quantitative analysis. The data was displayed by the histogram, scatter plot, and gallery functions of the same program. Fluorescent samples can be reliably detected, focused, and scanned. The measured integrated fluorescence showed linearity with exposure time and staining intensity. Automated fluorescent WSI with stable LED illumination and high-quality homogeneous fluorescent slides can be used conveniently for SBC.

Collagen XVII/BP180 protein expression in squamous cell carcinoma of the skin detected with novel monoclonal antibodies in archived tissues using tissue microarrays and digital microscopy.

Collagen XVII/BP180, a hemidesmosomal adhesion protein, is lost during normal keratinocyte maturation; however, it may be reexpressed upon malignant transformation. In this work, highly sensitive monoclonal antibodies 6D1 and 9G2 were produced, characterized, and used for the detection of collagen XVII in a tissue microarray series of archived samples of nonmelanocytic epithelial neoplasias, including 5 verruca vulgaris, 14 seborrheic keratosis, 38 actinic keratosis, 38 basal cell carcinoma (BCC), 15 basosquamous carcinoma, 58 squamous cell carcinoma (SCC), and 9 normal skin. Digital microscopy and a new tissue microarray software linking image and patient data allowed easy and validated evaluation and quality archiving of stained samples. In normal skin and benign epidermal lesions, collagen XVII protein was restricted to basal keratinocytes. However, possibly as a sign of undifferentiated/transformed state, it was widely expressed in SCC showing elevated levels around invasive tumor fronts with some staining in tumor adjacent stroma, endothelium, and histiocytes. Collagen XVII immunostaining of atypical keratinocytes in most actinic/solar keratosis supports the view of their malignancy and common origin with SCC. Squamous component of basosquamous carcinoma showed moderate reaction, whereas islets of BCC were mainly negative reflecting the diverse genotype and phenotype, and pathogenesis of SCC and BCC. These results suggest that collagen XVII neoexpression may be associated with early atypia/malignant transformation of keratinocytes. Further investigations are under way to analyze the potential of these antibodies for tracing progression and metastatic potential of skin tumors.

Automated classification of inflammation in colon histological sections based on digital microscopy and advanced image analysis.

Automated and quantitative histological analysis can improve diagnostic efficacy in colon sections. Our objective was to develop a parameter set for automated classification of aspecific colitis, ulcerative colitis, and Crohn's disease using digital slides, tissue cytometric parameters, and virtual microscopy. Routinely processed hematoxylin-and-eosin-stained histological sections from specimens that showed normal mucosa (24 cases), aspecific colitis (11 cases), ulcerative colitis (25 cases), and Crohn's disease (9 cases) diagnosed by conventional optical microscopy were scanned and digitized in high resolution (0.24 mum/pixel). Thirty-eight cytometric parameters based on morphometry were determined on cells, glands, and superficial epithelium. Fourteen tissue cytometric parameters based on ratios of tissue compartments were counted as well. Leave-one-out discriminant analysis was used for classification of the samples groups. Cellular morphometric features showed no significant differences in these benign colon alterations. However, gland related morphological differences (Gland Shape) for normal mucosa, ulcerative colitis, and aspecific colitis were found (P < 0.01). Eight of the 14 tissue cytometric related parameters showed significant differences (P < 0.01). The most discriminatory parameters were the ratio of cell number in glands and in the whole slide, biopsy/gland surface ratio. These differences resulted in 88% overall accuracy in the classification. Crohn's disease could be discriminated only in 56%. Automated virtual microscopy can be used to classify colon mucosa as normal, ulcerative colitis, and aspecific colitis with reasonable accuracy. Further developments of dedicated parameters are necessary to identify Crohn's disease on digital slides.

Automated virtual microscopy of gastric biopsies.

BACKGROUND: Automated virtual microscopy of specimens from gastrointestinal biopsies is based on cytometric parameters of digitized histological sections. To our knowledge, cytometric parameters of gastritis and of adenocarcinoma have yet to be fully characterized. Our objective was to classify gastritis and adenocarcinoma based on cytometric parameters. We hypothesized that automated virtual microscopy using this novel classification can reliably diagnose gastritis and adenocarcinoma. METHODS: Routinely processed hematoxylin-and-eosin-stained histological sections from specimens that showed normal mucosa (14 cases), gastritis (35 cases), and adenocarcinoma (30 cases) diagnosed by conventional optical microscopy were scanned and digitized at high resolution. Thirty-eight cytometric parameters based on density and morphometry were applied to glands and superficial epithelium. Twelve cytometric parameters based on cytologic detail were applied to individual cells. RESULTS: Statistically significant differences in cytometric parameters for normal mucosa, gastritis, and adenocarcinoma were found. The most discriminatory parameter was the ratio of the total number of cells to the number of interstitial cells. These differences correctly classified adenocarcinoma at 100% accuracy and overall correctness was 86%. CONCLUSIONS: We describe a novel method of analyzing gastric mucosal histology based on cytometric parameters. Automated virtual microscopy can be used to classify gastric mucosa as normal, gastritis, or adenocarcinoma with reasonable accuracy. Further research is necessary to determine whether automated virtual microscopy can subclassify gastric mucosal histology in greater detail.

Three-dimensional virtual microscopy of colorectal biopsies.

Conventional optical microscopy of specimens from colorectal biopsies commonly produces diagnostic errors due to incomplete sampling or poor orientation. Obtaining additional sections or re-embedding may help avoid these errors, but can prolong turnaround time. We describe new technology, which incorporates exhaustive sectioning, 3-dimensional reconstruction, and virtual microscopy, that may eliminate these problems by enabling pathologists to rapidly examine entire specimens and convert poorly oriented mucosa to well-oriented mucosa.