Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text]

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.

Case summary This report describes a cat with chronic, progressive, non-painful, non-lateralizing multifocal neurologic clinical signs associated with feline infectious peritonitis (FIP). The cat initially presented as underweight, despite a good appetite, and a complete blood count showed non-regenerative anemia. Three months later the cat was returned having developed ataxia and paraparesis, which then progressed over 2 months to tetraparesis, tail plegia, urinary and fecal incontinence, and titubation. Histologic examination of the tissues with subsequent immunohistochemistry confirmed FIP-associated meningoencephalomyelitis following necropsy. Molecular analysis of the coronavirus spike protein within the tissues identified a specific, functionally relevant amino acid change (R793M), which was only identified in tissues associated with the central nervous system (ie, brain and spinal cord). Relevance and novel information This case report describes an early presentation of a cat with primarily neurologic FIP, with molecular characterization of the virus within various tissues.

An aged mixed-breed goat doe was presented with a 9-mo history of serosanguineous vaginal discharge. Vaginal speculum examination revealed serosanguineous discharge but otherwise no abnormalities. Transrectal ultrasonography showed normal ovaries and multifocal cystic lesions within the uterus. Ovariohysterectomy was recommended because of a strong suspicion of neoplasia. Multiple, non-resectable masses were noted in and around the uterus intraoperatively, and euthanasia was elected. Autopsy revealed multiple masses within the uterus, cervix, and lung parenchyma. Histologically, the masses within the uterus represented a likely collision tumor of primary adenocarcinoma and leiomyosarcoma. Our report highlights the importance of obtaining biopsy samples of all masses because the lesions described showed significantly different biological behavior. This information is vital to guide treatment and prognosis.