Canine mammary tumors (CMTs) represent a serious issue in worldwide veterinary practice and several risk factors are variably implicated in the biology of CMTs. The present study examines the relationship between risk factors and histological diagnosis of a large CMT dataset from three academic institutions by classical statistical analysis and supervised machine learning methods. Epidemiological, clinical, and histopathological data of 1866 CMTs were included. Dogs with malignant tumors were significantly older than dogs with benign tumors (9.6 versus 8.7 years, p < 0.001). Malignant tumors were significantly larger than benign counterparts (2.69 versus 1.7 cm, p < 0.001). Interestingly, 18% of malignant tumors were smaller than 1 cm in diameter, providing compelling evidence that the size of the tumor should be reconsidered during the assessment of the TNM-WHO clinical staging. The application of the logistic regression and the machine learning model identified the age and the tumor’s size as the best predictors with an overall diagnostic accuracy of 0.63, suggesting that these risk factors are sufficient but not exhaustive indicators of the malignancy of CMTs. This multicenter study increases the general knowledge of the main epidemiologica-clinical risk factors involved in the onset of CMTs and paves the way for further investigations of these factors in association with CMTs and in the application of machine learning technology.

Porcine respiratory disease complex (PRDC) represents a significant threat to the swine industry, causing economic losses in pigs worldwide. Recently, beyond the endemic viruses PRRSV and PCV2, emerging viruses such as TTSuV, PCV3, and PPV2, have been associated with PRDC, but their role remains unclear. This study investigates the presence of PCV2 and PRRSV and emerging viruses (PCV3, TTSuV, and PPV2) in the lungs of swine belonging to different age groups by histopathology and real-time PCR. The prevalent lung lesion was interstitial pneumonia with increased severity in post-weaning pigs. PRRSV was detected in 33% of piglets’ lungs and in 20% of adults and post-weaning pigs with high Ct, while PCV2 was found in 100% of adult pigs, 33% of post-weaning pigs, and 22% of piglets, with low Ct in post-weaning pigs. PCV3 was present in all categories and coexisted with other viruses. TTSuV was detected in all swine in combination with other viruses, possibly influencing the disease dynamics, while PPV2 was detected in 100% of adults’ and 90% of piglets’ lungs. The detection of TTSuV, PCV3, and PPV2 in affected pigs prioritizes the need for comprehensive approaches in implementing appropriate control measures and minimizing economic losses associated with PRDC.

Histopathology, the gold-standard technique in classifying canine mammary tumors (CMTs), is a time-consuming process, affected by high inter-observer variability. Digital (DP) and Computer-aided pathology (CAD) are emergent fields that will improve overall classification accuracy. In this study, the ability of the CAD systems to distinguish benign from malignant CMTs has been explored on a dataset—namely CMTD—of 1056 hematoxylin and eosin JPEG images from 20 benign and 24 malignant CMTs, with three different CAD systems based on the combination of a convolutional neural network (VGG16, Inception v3, EfficientNet), which acts as a feature extractor, and a classifier (support vector machines (SVM) or stochastic gradient boosting (SGB)), placed on top of the neural net. Based on a human breast cancer dataset (i.e., BreakHis) (accuracy from 0.86 to 0.91), our models were applied to the CMT dataset, showing accuracy from 0.63 to 0.85 across all architectures. The EfficientNet framework coupled with SVM resulted in the best performances with an accuracy from 0.82 to 0.85. The encouraging results obtained by the use of DP and CAD systems in CMTs provide an interesting perspective on the integration of artificial intelligence and machine learning technologies in cancer-related research.