A Statistical Analysis of Risk Factors and Biological Behavior in Canine Mammary Tumors: A Multicenter Study

SIMPLE SUMMARY: The increase in the incidence of neoplastic disease represents a relentless challenge in veterinary medicine, and many efforts aimed to increase early diagnosis and life perspective have been made. Canine mammary tumors are the most common neoplasm and one of the leading causes of death in female dogs. Using a large number of data from three academic institutions, we found that dogs with malignant tumors were significantly older than dogs harboring benign tumors and that malignant tumors were significantly larger than benign counterparts. Moreover, a consistent fraction of malignant tumors is smaller than 1 cm, providing compelling evidence that the size of mammary tumors is a critical but easily detectable, indirect prognostic-related, clinical factor. We suggest that the control of cancer-related risk factors represents one of the most compelling prevention strategies and paves the way for further investigations. ABSTRACT: 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.