Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas

In dogs, the BRAF mutation (V595E) is common in bladder and prostate cancer and represents a specific diagnostic marker. Recent advantages in artificial intelligence (AI) offer new opportunities in the field of tumour marker detection. While AI histology studies have been conducted in humans to detect BRAF mutation in cancer, comparable studies in animals are lacking. In this study, we used commercially available AI histology software to predict BRAF mutation in whole slide images (WSI) of bladder urothelial carcinomas (UC) stained with haematoxylin and eosin (HE), based on a training (n = 81) and a validation set (n = 96). Among 96 WSI, 57 showed identical PCR and AI-based BRAF predictions, resulting in a sensitivity of 58% and a specificity of 63%. The sensitivity increased substantially to 89% when excluding small or poor-quality tissue sections. Test reliability depended on tumour differentiation (p < 0.01), presence of inflammation (p < 0.01), slide quality (p < 0.02) and sample size (p < 0.02). Based on a small subset of cases with available adjacent non-neoplastic urothelium, AI was able to distinguish malignant from benign epithelium. This is the first study to demonstrate the use of AI histology to predict BRAF mutation status in canine UC. Despite certain limitations, the results highlight the potential of AI in predicting molecular alterations in routine tissue sections

Artificial Intelligence to Predict the BRAF V595E Mutation in Canine Urinary Bladder Urothelial Carcinomas.

In dogs, the BRAF mutation (V595E) is common in bladder and prostate cancer and represents a specific diagnostic marker. Recent advantages in artificial intelligence (AI) offer new opportunities in the field of tumour marker detection. While AI histology studies have been conducted in humans to detect BRAF mutation in cancer, comparable studies in animals are lacking. In this study, we used commercially available AI histology software to predict BRAF mutation in whole slide images (WSI) of bladder urothelial carcinomas (UC) stained with haematoxylin and eosin (HE), based on a training (n = 81) and a validation set (n = 96). Among 96 WSI, 57 showed identical PCR and AI-based BRAF predictions, resulting in a sensitivity of 58% and a specificity of 63%. The sensitivity increased substantially to 89% when excluding small or poor-quality tissue sections. Test reliability depended on tumour differentiation (p < 0.01), presence of inflammation (p < 0.01), slide quality (p < 0.02) and sample size (p < 0.02). Based on a small subset of cases with available adjacent non-neoplastic urothelium, AI was able to distinguish malignant from benign epithelium. This is the first study to demonstrate the use of AI histology to predict BRAF mutation status in canine UC. Despite certain limitations, the results highlight the potential of AI in predicting molecular alterations in routine tissue sections

4MOST: Project overview and information for the First Call for Proposals

We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Visible and Infrared Survey Telescope for Astronomy (VISTA) at Paranal. Its key specifications are: a large field of view (FoV) of 4.2 square degrees and a high multiplex capability, with 1624 fibres feeding two low-resolution spectrographs ($R = \lambda/\Delta\lambda \sim 6500$), and 812 fibres transferring light to the high-resolution spectrograph ($R \sim 20\,000$). After a description of the instrument and its expected performance, a short overview is given of its operational scheme and planned 4MOST Consortium science; these aspects are covered in more detail in other articles in this edition of The Messenger. Finally, the processes, schedules, and policies concerning the selection of ESO Community Surveys are presented, commencing with a singular opportunity to submit Letters of Intent for Public Surveys during the first five years of 4MOST operations

Feline primary nonhematopoietic malignant liver tumors: A multicenter retrospective study (2000-2021).

There is scant literature on primary nonhematopoietic malignant liver tumors (PMLT) in cats. In this retrospective study, medical data of 40 cats diagnosed with PMLT were reviewed over a period of 22 years (2000-2021). The most frequent epithelial tumors were hepatocellular (42.5%) and bile duct carcinomas (32.5%), only six (15%) cats had mesenchymal tumors. The median age was 13 years and clinical signs commonly included ano-/hyporexia (62.5%), apathy/lethargy (52.5%), weight loss (42.5%) and vomiting (35%). At initial diagnosis, metastases were confirmed in 1 (2.5%) and suspected in three (7.5%) cats. Massive was the most frequent morphology (75%). Most intrahepatic tumors were left-sided (54.2%) with the left medial lobe being primarily affected (25%). Extrahepatic tumors were rare (5%). In 34 (85%) cats, liver lobectomy was performed (surgery group), four (10%) were treated palliatively (non-surgery group), and two (5%) received no treatment. Intraoperative complications occurred in 11.8% with four (15.4%) postoperative deaths. Recurrence was detected in 28.6% at a median of 151 days (range, 79-684 days), while postoperative metastases were suspected in 21.4% at a median of 186 days (range, 79-479 days). The median survival time (MST) was significantly longer in cats of the surgery group (375 days) than in the non-surgery group (16 days) (P = 0.002). MST was 868 days for hepatocellular compared to 270 days for bile duct carcinomas (P = 0.06). In summary, liver lobectomy is associated with prolonged survival times and good prognosis in cats with hepatocellular, and an acceptable prognosis in cats with bile duct carcinoma