Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats.

Digital pathology evolved rapidly, enabling more systematic usage of image analysis and development of artificial intelligence (AI) applications. Here, combined AI models were developed to evaluate hepatocellular hypertrophy in rat liver, using commercial AI-based software on hematoxylin and eosin-stained whole slide images. In a first approach, deep learning-based identification of critical tissue zones (centrilobular, midzonal, and periportal) enabled evaluation of region-specific cell size. Mean cytoplasmic area of hepatocytes was calculated via several sequential algorithms including segmentation in microanatomical structures (separation of sinusoids and vessels from hepatocytes), nuclear detection, and area measurements. An increase in mean cytoplasmic area could be shown in groups given phenobarbital, known to induce hepatocellular hypertrophy when compared to control groups, in multiple studies. Quantitative results correlated with the gold standard: observation and grading performed by board-certified veterinary pathologists, liver weights, and gene expression. Furthermore, as a second approach, we introduce for the first time deep learning-based direct detection of hepatocellular hypertrophy with similar results. Cell hypertrophy is challenging to pick up, particularly in milder cases. Additional evaluation of mean cytoplasmic area or direct detection of hypertrophy, combined with histopathological observations and liver weights, is expected to increase accuracy and repeatability of diagnoses and grading by pathologists.

Quantification and characterization of radical production in human, animal and 3D skin models during sun irradiation measured by EPR spectroscopy.

Sun radiation is indispensable to our health, however, a long term and high exposure could lead to erythema, premature skin aging and promotion of skin tumors. An underlying pathomechanism is the formation of free radicals. First, reactive oxygen species (*OH, *O2-) and then, secondary lipid oxygen species (C centered radicals, CCR) are formed. A high amount of free radicals results in oxidative stress with subsequent cell damage. In dermatological research different skin models are used, however, comparative data about the cutaneous radical formation are missing. In this study, the radical formation in porcine-, (SKH-1) murine-, human- ex vivo skin and reconstructed human skin (RHS) were investigated during simulated sun irradiation (305-2200 nm), with X-band EPR spectroscopy. The amount of radical formation was investigated with the spin probe PCA exposed to a moderate sun dose below one minimal erythema dose (MED, ~25 mJ/cm2 UVB) in all skin models. Furthermore, the *OH and *CCR radical concentrations were measured with the spin trap DMPO within 0-4 MED (porcine-, human skin and RHS). The highest amount of radicals was found in RHS followed by murine and porcine, and the lowest amount in human ex vivo skin. In all skin models, more *OH than CCR radicals were found at 0-4 MED. Additionally, this work addresses the limitations in the characterization with the spin trap DMPO. The measurements have shown that the most comparable skin model to in vivo human skin could differ depending on the focus of the investigation. If the amount of radial production is regarded, RHS seems to be in a similar range like in vivo human skin. If the investigation is focused on the radical type, porcine skin is most comparable to ex vivo human skin, at an irradiation dose not exceeding 1 MED. Here, no comparison to in vivo human skin is possible.

Validation of Digital Microscopy Compared With Light Microscopy for the Diagnosis of Canine Cutaneous Tumors.

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.