Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs

Nuclear Morphometry using a Deep Learning-based Algorithm has Prognostic Relevance for Canine Cutaneous Mast Cell Tumors

Variation in nuclear size and shape is an important criterion of malignancy for many tumor types; however, categorical estimates by pathologists have poor reproducibility. Measurements of nuclear characteristics (morphometry) can improve reproducibility, but manual methods are time consuming. In this study, we evaluated fully automated morphometry using a deep learning-based algorithm in 96 canine cutaneous mast cell tumors with information on patient survival. Algorithmic morphometry was compared with karyomegaly estimates by 11 pathologists, manual nuclear morphometry of 12 cells by 9 pathologists, and the mitotic count as a benchmark. The prognostic value of automated morphometry was high with an area under the ROC curve regarding the tumor-specific survival of 0.943 (95% CI: 0.889 - 0.996) for the standard deviation (SD) of nuclear area, which was higher than manual morphometry of all pathologists combined (0.868, 95% CI: 0.737 - 0.991) and the mitotic count (0.885, 95% CI: 0.765 - 1.00). At the proposed thresholds, the hazard ratio for algorithmic morphometry (SD of nuclear area $\geq 9.0 \mu m^2$) was 18.3 (95% CI: 5.0 - 67.1), for manual morphometry (SD of nuclear area $\geq 10.9 \mu m^2$) 9.0 (95% CI: 6.0 - 13.4), for karyomegaly estimates 7.6 (95% CI: 5.7 - 10.1), and for the mitotic count 30.5 (95% CI: 7.8 - 118.0). Inter-rater reproducibility for karyomegaly estimates was fair ($\kappa$ = 0.226) with highly variable sensitivity/specificity values for the individual pathologists. Reproducibility for manual morphometry (SD of nuclear area) was good (ICC = 0.654). This study supports the use of algorithmic morphometry as a prognostic test to overcome the limitations of estimates and manual measurements

Post Mortem Study on the Effects of Routine Handling and Manipulation of Laboratory Mice

Routine handling and manipulation of laboratory mice are integral components of most preclinical studies. Any type of handling and manipulation may cause stress and result in physical harm to mice, potentially leading to unintended consequences of experimental outcomes. Nevertheless, the pathological effects of these interventions are poorly documented and assumed to have a negligible effect on experimental variables. In that context, we provide a comprehensive post mortem overview of the main pathological changes associated with routine interventions (i.e., restraint, blood drawing, and intraperitoneal injections) of laboratory mice with an emphasis on presumed traumatic osteoarticular lesions. A total of 1000 mice from various studies were included, with 864 animals being heavily manipulated and 136 being handled for routine husbandry procedures only. The most common lesions observed were associated with blood collection or intraperitoneal injections, as well as a series of traumatic osteoarticular lesions likely resulting from restraint. Osteoarticular lesions were found in 62 animals (61 heavily manipulated; 1 unmanipulated) with rib fractures and avulsion of the dens of the axis being over-represented. Histopathology and micro-CT confirmed the traumatic nature of the rib fractures. While these lesions might be unavoidable if mice are manipulated according to the current standards, intentional training of research personnel on appropriate mouse handling and restraint techniques could help reduce their frequency and the impact on animal wellbeing as well as study reproducibility

Classification and Grading of Melanocytic Lesions in a Mouse Model of NRAS-driven Melanomagenesis

The mouse line carrying the Tg(Tyr-NRAS*Q61K)1Bee transgene is widely used to model in vivo NRAS-driven melanomagenesis. Although the pathological features of this model are well described, classification and interpretation of the resulting proliferative lesions—including their origin, evolution, grading, and pathobiological significance—are still unclear and not supported by molecular and biological evidence. Focusing on their classification and grading, this work combines histopathology and expression analysis (using both immunohistochemistry [IHC] and quantitative PCR) of selected biomarkers to study the full spectrum of cutaneous and lymph nodal melanocytic proliferations in the Tg(Tyr-NRAS*Q61K)1Bee mouse. The analysis of cutaneous and lymph nodal melanocytic proliferations has demonstrated that a linear correlation exists between tumor grade and Ki-67, microphthalmia-associated transcription factor (MITF), gp100, and nestin IHC, with a significantly increased expression in high-grade lesions compared with low-grade lesions. The accuracy of the assessment of MITF IHC in melanomas was also confirmed by quantitative PCR analysis. In conclusion, we believe the incorporation of MITF, Ki-67, gp100, and nestin analysis into the histopathological classification/grading scheme of melanocytic proliferations described for this model will help to assess with accuracy the nature and evolution of the phenotype, monitor disease progression, and predict response to experimental treatment or other preclinical manipulations.SCOPUS: ar.jDecretOANoAutActifinfo:eu-repo/semantics/publishe

Mitochondrial defects caused by PARL deficiency lead to arrested spermatogenesis and ferroptosis

Impaired spermatogenesis and male infertility are common manifestations associated with mitochondrial diseases, yet the underlying mechanisms linking these conditions remain elusive. In this study, we demonstrate that mice deficient for the mitochondrial intra-membrane rhomboid protease PARL, a recently reported model of the mitochondrial encephalopathy Leigh syndrome, develop early testicular atrophy caused by a complete arrest of spermatogenesis during meiotic prophase I, followed by degeneration and death of arrested spermatocytes. This process is independent of neurodegeneration. Interestingly, genetic modifications of PINK1, PGAM5, and TTC19 – three major substrates of PARL with important roles in mitochondrial homeostasis – fail to reproduce or modify this severe phenotype, indicating that the spermatogenic arrest arises from distinct molecular pathways. We further observed severe abnormalities in mitochondrial ultrastructure in PARL-deficient spermatocytes, along with prominent electron transfer chain defects, disrupted coenzyme Q (CoQ) biosynthesis, and metabolic rewiring. These mitochondrial defects are associated with a germ cell-specific decrease in GPX4 expression leading arrested spermatocytes to ferroptosis – a regulated cell death modality characterized by uncontrolled lipid peroxidation. Our results suggest that mitochondrial defects induced by PARL depletion act as an initiating trigger for ferroptosis in primary spermatocytes through simultaneous effects on GPX4 and CoQ – two major inhibitors of ferroptosis. These findings shed new light on the potential role of ferroptosis in the pathogenesis of mitochondrial diseases and male infertility warranting further investigation

Anti-inflammatory effects of hunger are transmitted to the periphery via projection-specific AgRP circuits

Summary: Caloric restriction has anti-inflammatory effects. However, the coordinated physiological actions that lead to reduced inflammation in a state of caloric deficit (hunger) are largely unknown. Using a mouse model of injury-induced peripheral inflammation, we find that food deprivation reduces edema, temperature, and cytokine responses that occur after injury. The magnitude of the anti-inflammatory effect that occurs during hunger is more robust than that of non-steroidal anti-inflammatory drugs. The effects of hunger are recapitulated centrally by activity in nutrient-sensing hypothalamic agouti-related protein (AgRP)-expressing neurons. We find that AgRP neurons projecting to the paraventricular nucleus of the hypothalamus rapidly and robustly reduce inflammation and mediate the majority of hunger’s anti-inflammatory effects. Intact vagal efferent signaling is required for the anti-inflammatory action of hunger, revealing a brain-to-periphery pathway for this reduction in inflammation. Taken together, these data begin to unravel a potent anti-inflammatory pathway engaged by hypothalamic AgRP neurons to reduce inflammation

Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics

Machine learning for medical imaging not only requires sufficient amounts of data for training and testing but also that the data be independent. It is common to see highly interdependent data whenever there are inherent correlations between observations. This is especially to be expected for sequential imaging data taken from time series. In this study, we evaluate the use of statistical measures to test the independence of sequential ultrasound image data taken from the same case. A total of 1180 B-mode liver ultrasound images with 5903 regions of interests were analyzed. The ultrasound images were taken from two liver disease groups, fibrosis and steatosis, as well as normal cases. Computer-extracted texture features were then used to train a machine learning (ML) model for computer-aided diagnosis. The experiment resulted in high two-category diagnosis using logistic regression, with AUC of 0.928 and high performance of multicategory classification, using random forest ML, with AUC of 0.917. To evaluate the image region independence for machine learning, Jenson–Shannon (JS) divergence was used. JS distributions showed that images of normal liver were independent from each other, while the images from the two disease pathologies were not independent. To guarantee the generalizability of machine learning models, and to prevent data leakage, multiple frames of image data acquired of the same object should be tested for independence before machine learning. Such tests can be applied to real-world medical image problems to determine if images from the same subject can be used for training

sj-xlsx-2-vet-10.1177_03009858231222216 – Supplemental material for Humanization with CD34-positive hematopoietic stem cells in NOG-EXL mice results in improved long-term survival and less severe myeloid cell hyperactivation phenotype relative to NSG-SGM3 mice

Supplemental material, sj-xlsx-2-vet-10.1177_03009858231222216 for Humanization with CD34-positive hematopoietic stem cells in NOG-EXL mice results in improved long-term survival and less severe myeloid cell hyperactivation phenotype relative to NSG-SGM3 mice by Elinor Willis, Jillian Verrelle, Esha Banerjee, Charles-Antoine Assenmacher, James C. Tarrant, Nicholas Skuli, Moriah L. Jacobson, Donald M. O’Rouke, Zev A. Binder and Enrico Radaelli in Veterinary Pathology</p