BackgroundCalprotectin is a marker of inflammation, but its clinical utility in dogs with chronic inflammatory enteropathies (CIE) is unknown.ObjectiveEvaluation of fecal calprotectin in dogs with biopsy‐confirmed CIE.Animals127 dogs.MethodsProspective case‐control study. Dogs were assigned a canine chronic enteropathy clinical activity index (CCECAI) score, and histologic lesions severity was assessed. Fecal calprotectin, fecal S100A12, and serum C‐reactive protein (CRP) were measured. Food‐ or antibiotic‐responsive cases (FRE/ARE, n = 13) were distinguished from steroid‐/immunosuppressant‐responsive or ‐refractory cases (SRE/IRE, n = 20). Clinical response to treatment in SRE/IRE dogs was classified as complete remission (CR), partial response (PR), or no response (NR).ResultsFecal calprotectin correlated with CCECAI (ρ = 0.27, P = .0065) and fecal S100A12 (ρ = 0.90, P < .0001), some inflammatory criteria, and cumulative inflammation scores, but not serum CRP (ρ = 0.16, P = .12). Dogs with SRE/IRE had higher fecal calprotectin concentrations (median: 2.0 μg/g) than FRE/ARE dogs (median: 1.4 μg/g), and within the SRE/IRE group, dogs with PR/NR had higher fecal calprotectin (median: 37.0 μg/g) than dogs with CR (median: 1.6 μg/g). However, both differences did not reach statistical significance (both P = .10). A fecal calprotectin ≥15.2 μg/g separated both groups with 80% sensitivity (95% confidence interval [95%CI]: 28%‐100%) and 75% specificity (95%CI: 43%‐95%).Conclusions and Clinical ImportanceFecal calprotectin could be a useful surrogate marker of disease severity in dogs with CIE, but larger longitudinal studies are needed to evaluate its utility in predicting the response to treatment.

Integration of new technologies, such as digital microscopy, into a highly standardized laboratory routine requires the validation of its performance in terms of reliability, specificity, and sensitivity. However, a validation study of digital microscopy is currently lacking in veterinary pathology. The aim of the current study was to validate the usability of digital microscopy in terms of diagnostic accuracy, speed, and confidence for diagnosing and differentiating common canine cutaneous tumor types and to compare it to classical light microscopy. Therefore, 80 histologic sections including 17 different skin tumor types were examined twice as glass slides and twice as digital whole-slide images by 6 pathologists with different levels of experience at 4 time points. Comparison of both methods found digital microscopy to be noninferior for differentiating individual tumor types within the category epithelial and mesenchymal tumors, but diagnostic concordance was slightly lower for differentiating individual round cell tumor types by digital microscopy. In addition, digital microscopy was associated with significantly shorter diagnostic time, but diagnostic confidence was lower and technical quality was considered inferior for whole-slide images compared with glass slides. Of note, diagnostic performance for whole-slide images scanned at 200× magnification was noninferior in diagnostic performance for slides scanned at 400×. In conclusion, digital microscopy differs only minimally from light microscopy in few aspects of diagnostic performance and overall appears adequate for the diagnosis of individual canine cutaneous tumors with minor limitations for differentiating individual round cell tumor types and grading of mast cell tumors.

Manual count of mitotic figures, which is determined in the tumor region with the highest mitotic activity, is a key parameter of most tumor grading schemes. It can be, however, strongly dependent on the area selection due to uneven mitotic figure distribution in the tumor section. We aimed to assess the question, how significantly the area selection could impact the mitotic count, which has a known high inter-rater disagreement. On a data set of 32 whole slide images of H&E-stained canine cutaneous mast cell tumor, fully annotated for mitotic figures, we asked eight veterinary pathologists (five board-certified, three in training) to select a field of interest for the mitotic count. To assess the potential difference on the mitotic count, we compared the mitotic count of the selected regions to the overall distribution on the slide. Additionally, we evaluated three deep learning-based methods for the assessment of highest mitotic density: In one approach, the model would directly try to predict the mitotic count for the presented image patches as a regression task. The second method aims at deriving a segmentation mask for mitotic figures, which is then used to obtain a mitotic density. Finally, we evaluated a two-stage object-detection pipeline based on state-of-the-art architectures to identify individual mitotic figures. We found that the predictions by all models were, on average, better than those of the experts. The two-stage object detector performed best and outperformed most of the human pathologists on the majority of tumor cases. The correlation between the predicted and the ground truth mitotic count was also best for this approach (0.963–0.979). Further, we found considerable differences in position selection between pathologists, which could partially explain the high variance that has been reported for the manual mitotic count. To achieve better inter-rater agreement, we propose to use a computer-based area selection for support of the pathologist in the manual mitotic count.

Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in ten consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists.Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10-hpf-areas) and 9 borderline cases with variable MCs around 7, which is a cut-off value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the three ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC≥7.Falsely low MCs below 7 mainly occurred in 4/9 borderline cases that had very few ground truth areas with MC≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC. Keywords: Area selection, high-power field, mitotic activity, mitotic figure distribution, tumor grading, tumor periphery. 3 Aubreville M, Bertram CA, Klopfleisch R, Maier A. Field of interest proposal for augmented mitotic cell count: Comparison of two convolutional networks.