Quantification of Estrogen Receptor-Alpha Expression in Human Breast Carcinomas With a Miniaturized, Low-Cost Digital Microscope: A Comparison with a High-End Whole Slide-Scanner.

INTRODUCTION: A significant barrier to medical diagnostics in low-resource environments is the lack of medical care and equipment. Here we present a low-cost, cloud-connected digital microscope for applications at the point-of-care. We evaluate the performance of the device in the digital assessment of estrogen receptor-alpha (ER) expression in breast cancer samples. Studies suggest computer-assisted analysis of tumor samples digitized with whole slide-scanners may be comparable to manual scoring, here we study whether similar results can be obtained with the device presented. MATERIALS AND METHODS: A total of 170 samples of human breast carcinoma, immunostained for ER expression, were digitized with a high-end slide-scanner and the point-of-care microscope. Corresponding regions from the samples were extracted, and ER status was determined visually and digitally. Samples were classified as ER negative (<1% ER positivity) or positive, and further into weakly (1-10% positivity) and strongly positive. Interobserver agreement (Cohen's kappa) was measured and correlation coefficients (Pearson's product-momentum) were calculated for comparison of the methods. RESULTS: Correlation and interobserver agreement (r = 0.98, p < 0.001, kappa = 0.84, CI95% = 0.75-0.94) were strong in the results from both devices. Concordance of the point-of-care microscope and the manual scoring was good (r = 0.94, p < 0.001, kappa = 0.71, CI95% = 0.61-0.80), and comparable to the concordance between the slide scanner and manual scoring (r = 0.93, p < 0.001, kappa = 0.69, CI95% = 0.60-0.78). Fourteen (8%) discrepant cases between manual and device-based scoring were present with the slide scanner, and 16 (9%) with the point-of-care microscope, all representing samples of low ER expression. CONCLUSIONS: Tumor ER status can be accurately quantified with a low-cost imaging device and digital image-analysis, with results comparable to conventional computer-assisted or manual scoring. This technology could potentially be expanded for other histopathological applications at the point-of-care.

Grading of neuroendocrine tumors with Ki-67 requires high-quality assessment practices.

Gastrointestinal and pancreatic neuroendocrine tumors (NETs) arise from disseminated neuroendocrine cells, expressing general and specific neuroendocrine markers. The World Health Organization 2010 classification of NETs is based on grading them according to the proliferation index (PI), which is determined by immunohistochemical staining of the nuclear antigen Ki-67. The classification introduces Ki-67 as the most important criterion for tumor grading, influencing patients' prognoses and the choice of treatment. The aim of this study was to evaluate the assessment of PI value in NETs and its influence on tumor grading. The tumor material consisted of 51 NETs from the pancreas (n=31) and ileum (n=20). The slides were stained with the Ki-67 antibody and visualized using a polymer kit. PI was assessed visually by microscope oculars and using a public domain image analysis software, ImmunoRatio. The PI was measured from the most proliferative areas of the tumor. The PI values and tumor grade by ImmunoRatio were highly reproducible as compared with conventional assessment, which suffered from variation especially if ascertained by different observers. Computer-aided assessments had almost perfect correlation (r=0.985, r=0.987, and r=0.995) (P=0.000) and reproducibility (κ=0.886, κ=0.886, and κ=1.000) (P=0.000) in PI values and tumor grades, respectively. The PI values and tumor grade between conventional and ImmunoRatio assessments by a qualified observer were in good agreement. ImmunoRatio is a qualified diagnostic aid to more objectively analyze Ki-67 PI-based tumor grade in NETs.

Development and evaluation of a virtual microscopy application for automated assessment of Ki-67 expression in breast cancer.

BACKGROUND: The aim of the study was to develop a virtual microscopy enabled method for assessment of Ki-67 expression and to study the prognostic value of the automated analysis in a comprehensive series of patients with breast cancer. METHODS: Using a previously reported virtual microscopy platform and an open source image processing tool, ImageJ, a method for assessment of immunohistochemically (IHC) stained area and intensity was created. A tissue microarray (TMA) series of breast cancer specimens from 1931 patients was immunostained for Ki-67, digitized with a whole slide scanner and uploaded to an image web server. The extent of Ki-67 staining in the tumour specimens was assessed both visually and with the image analysis algorithm. The prognostic value of the computer vision assessment of Ki-67 was evaluated by comparison of distant disease-free survival in patients with low, moderate or high expression of the protein. RESULTS: 1648 evaluable image files from 1334 patients were analysed in less than two hours. Visual and automated Ki-67 extent of staining assessments showed a percentage agreement of 87% and weighted kappa value of 0.57. The hazard ratio for distant recurrence for patients with a computer determined moderate Ki-67 extent of staining was 1.77 (95% CI 1.31-2.37) and for high extent 2.34 (95% CI 1.76-3.10), compared to patients with a low extent. In multivariate survival analyses, automated assessment of Ki-67 extent of staining was retained as a significant prognostic factor. CONCLUSIONS: Running high-throughput automated IHC algorithms on a virtual microscopy platform is feasible. Comparison of visual and automated assessments of Ki-67 expression shows moderate agreement. In multivariate survival analysis, the automated assessment of Ki-67 extent of staining is a significant and independent predictor of outcome in breast cancer.