Establishment of digital cutoff values for intraepithelial lymphocytes in biopsies from colonic mucosa with lymphocytic colitis.

BACKGROUND/INTRODUCTION: Lymphocytic colitis (LC) and the incomplete form (LCi) are common causes of chronic watery diarrhea. Endoscopy is often inconspicuous, and the diagnosis relies on histopathological assessment of colonic biopsies. Digital Image Analysis (DIA) eliminates interobserver variation. The aim of this study was to establish digital cutoff values for LC and LCi on CD3 stained slides. MATERIAL AND METHODS: One hundred and six patients with a hematoxylin and eosin (HE) diagnosis of normal colonic mucosa (N = 19), non-specific reactive changes (N = 24), LCi (N = 23) and LC (N = 40) were eligible for analysis. The number of intraepithelial lymphocytes (IELs) reached by DIA in the total surface epithelium and in hot spots of the biopsies was compared with the diagnostic category assigned by the pathologists based on HE stained slides. The digitalized slides were analyzed for number of IELs using Visiopharm Quantitative Digital Pathology software. All digitalized slides were examined manually to identify differences in the approach to the evaluation of the biopsies by the pathologists and DIA. RESULTS: The median IEL counts and interquartile range in the total surface epithelium were 3.6 (3.2-4.3), 4.4 (3.4-5.3), 19.8 (16.6-30.0) and 41.3 (37.0-47.8) in normal colon mucosa, mucosa with non-specific reactive changes, LCi and LC, respectively. Discrimination between normal mucosa and non-specific reactive changes was not possible. Digital cutoff values with the best separation between non-LC, LCi and LC were > 13 IELs/100 epithelial cells for LCi and > 36 IELs/100 epithelial cells for LC. These cutoff values resulted in an agreement between the pathologist's and DIA that was very good with a kappa value of 0.90. CONCLUSION: Despite differences among the approach of DIA and the pathologist's assessment of IELs in colonic mucosa DIA is able discriminate between the HE based diagnoses of the three subgroups non-LC, LCi and LC with high accuracy.

Computer-assisted stereology and automated image analysis for quantification of tumor infiltrating lymphocytes in colon cancer.

BACKGROUND: Precise prognostic and predictive variables allowing improved post-operative treatment stratification are missing in patients treated for stage II colon cancer (CC). Investigation of tumor infiltrating lymphocytes (TILs) may be rewarding, but the lack of a standardized analytic technique is a major concern. Manual stereological counting is considered the gold standard, but digital pathology with image analysis is preferred due to time efficiency. The purpose of this study was to compare manual stereological estimates of TILs with automatic counts obtained by image analysis, and at the same time investigate the heterogeneity of TILs. METHODS: From 43 patients treated for stage II CC in 2002 three paraffin embedded, tumor containing tissue blocks were selected one of them representing the deepest invasive tumor front. Serial sections from each of the 129 blocks were immunohistochemically stained for CD3 and CD8, and the slides were scanned. Stereological estimates of the numerical density and area fraction of TILs were obtained using the computer-assisted newCAST stereology system. For the image analysis approach an app-based algorithm was developed using Visiopharm Integrator System software. For both methods the tumor areas of interest (invasive front and central area) were manually delineated by the observer. RESULTS: Based on all sections, the Spearman's correlation coefficients for density estimates varied from 0.9457 to 0.9638 (p < 0.0001), whereas the coefficients for area fraction estimates ranged from 0.9400 to 0.9603 (P < 0.0001). Regarding heterogeneity, intra-class correlation coefficients (ICC) for CD3+ TILs varied from 0.615 to 0.746 in the central area, and from 0.686 to 0.746 in the invasive area. ICC for CD8+ TILs varied from 0.724 to 0.775 in the central area, and from 0.746 to 0.765 in the invasive area. CONCLUSIONS: Exact objective and time efficient estimates of numerical densities and area fractions of CD3+ and CD8+ TILs in stage II colon cancer can be obtained by image analysis and are highly correlated to the corresponding estimates obtained by the gold standard based on stereology. Since the intra-tumoral heterogeneity was low, this method may be recommended for quantifying TILs in only one histological section representing the deepest invasive tumor front.

Application of automated image analysis reduces the workload of manual screening of sentinel lymph node biopsies in breast cancer.

AIMS: Breast cancer is one of the most common cancer diseases in women, with >1.67 million cases being diagnosed worldwide each year. In breast cancer, the sentinel lymph node (SLN) pinpoints the first lymph node(s) into which the tumour spreads, and it is usually located in the ipsilateral axilla. In patients with no clinical signs of metastatic disease in the axilla, an SLN biopsy (SLNB) is performed. Assessment of metastases in the SLNB, when using a conventional microscope, is performed by manually observing a metastasis and measuring its size and/or counting the number of tumour cells. This is done essentially to categorize the type of metastasis as macrometastasis, micrometastasis, or isolated tumour cells, which is used to determine which treatment the breast cancer patient will benefit most from. The aim of this study was to evaluate whether digital image analysis can be applied as a screening tool for SNLB assessment without compromising the diagnostic accuracy. MATERIALS AND RESULTS: Consecutive SLNBs from 135 patients with localized breast cancer receiving surgery in the period February to August 2015 were collected and included in this study. Of the 135 patients, 35 were received at the Department of Pathology, Rigshospitalet, Copenhagen University Hospital, 50 at the Department of Pathology, Zealand University Hospital, and 50 at the Department of Pathology, Odense University Hospital. Formalin-fixed paraffin-embedded tissue sections were analysed by immunohistochemistry with the BenchMark ULTRA Ventana platform. Rigshospitalet used a mixture of cytokeratin (CK) 7 and CK19, Zealand University Hospital used pancytokeratin AE1/AE3 and Odense used pancytokeratin CAM5.2 for detection of epithelial tumour cells. Slides were stained locally. SLNB sections were assessed in a conventional microscope according to national guidelines for SLNBs in breast cancer patients. The immunohistochemically stained sections were scanned with a Hamamatsu NanoZoomer-XR digital whole slide scanner, and the images were analysed with Visiopharm's software by use of a custom-made algorithm for SLNBs in breast cancer. The algorithm was optimized to the CK antibodies and the local laboratory conditions, on the basis of staining intensity and background staining. Conventional microscopy was used as the gold standard for assessment of positive tumour cells, and the results were compared with those from digital image analysis. The algorithm showed a sensitivity of 100% (that is, no false-negative slides were observed), including 67.2%, 19.2% and 56.1% of the slides from the three pathology departments being negative, respectively. This means that, on average, the workload could have been decreased by 58.2% by use of the digital SLNB algorithm as a screening tool. CONCLUSIONS: The SLNB algorithm showed a sensitivity of 100% regardless of the antibody used for immunohistochemistry and the staining protocol. No false-negative slides were observed, which proves that the SLNB algorithm is an ideal screening tool for selecting those slides that a pathologist does not need to see. The implementation of automated digital image analysis of SLNBs in breast cancer would decrease the workload in this context for examining pathologists by almost 60%.

Optimizing HER2 assessment in breast cancer: application of automated image analysis.

In breast cancer, analysis of HER2 expression is pivotal for treatment decision. This study aimed at comparing digital, automated image analysis with manual reading using the HER2-CONNECT algorithm (Visiopharm) in order to minimize the number of equivocal 2+ scores and the need for reflex fluorescence in situ hybridization (FISH) analysis. Consecutive samples from 462 patients were included. Tissue micro arrays (TMAs) were routinely manufactured including two 2 mm cores from each patient, and each core was assessed in order to ensure the presence of invasive carcinoma. Immunohistochemical staining (IHC) was performed with Roche/Ventana's HER2 ready-to-use kit. TMAs were scanned in a Zeiss Axio Z1 scanner, and one batch analysis of the HER2-CONNECT algorithm including all core samples was run using Visiopharm's cloud-based software. The automated reading was compared to conventional manual assessment of HER2 protein expression, together with FISH analysis of HER2 gene amplification for borderline (2+) protein expression samples. Compared to FISH analysis, manual assessment of the HER2 protein expression demonstrated a sensitivity of 85.8% and a specificity of 86.0% with 14.0% equivocal samples. With HER2-CONNECT, sensitivity increased to 100 % and specificity to 95.5% with less than 4.5% equivocal. Total agreement when comparing HER2-CONNECT with manual IHC assessment supplemented by FISH for borderline (2+) cases was 93.6%. Application of automated image analysis for HER2 protein expression instead of manual assessment decreases the need for supplementary FISH testing by 68%. In the routine diagnostic setting, this would have significant impact on cost reduction and turn-around time.