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.

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%.

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BackgroundPrecise 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.MethodsFrom 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.ResultsBased 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.ConclusionsExact 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.Electronic supplementary materialThe online version of this article (10.1186/s13000-017-0653-0) contains supplementary material, which is available to authorized users.

PurposeThe aim of this study was to develop an automated image analysis software to measure the thickness of the subepithelial collagenous band in colon biopsies with collagenous colitis (CC) and incomplete CC (CCi). The software measures the thickness of the collagenous band on microscopic slides stained with Van Gieson (VG).Patients and methodsA training set consisting of ten biopsies diagnosed as CC, CCi, and normal colon mucosa was used to develop the automated image analysis (VG app) to match the assessment by a pathologist. The study set consisted of biopsies from 75 patients. Twenty-five cases were primarily diagnosed as CC, 25 as CCi, and 25 as normal or near-normal colonic mucosa. Four pathologists individually reassessed the biopsies and categorized all into one of the abovementioned three categories. The result of the VG app was correlated with the diagnosis provided by the four pathologists.ResultsThe interobserver agreement for each pair of pathologists ranged from κ-values of 0.56–0.81, while the κ-value for the VG app vs each of the pathologists varied from 0.63 to 0.79. The overall agreement between the four pathologists was κ=0.69, while the overall agreement between the four pathologists and the VG app was κ=0.71.ConclusionIn conclusion, the Visiopharm VG app is able to measure the thickness of a sub-epithelial collagenous band in colon biopsies with an accuracy comparable to the performance of a pathologist and thereby provides a promising supplementary tool for the diagnosis of CC and CCi and in particular for research.