A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features - Burti, Silvia; Zotti, Alessandro; Bonsembiante, Federico; Contiero, Barbara; Banzato, Tommaso (Frontiers in Veterinary Science, 2022)
The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were included in the study. Several qualitative and quantitative CT features were described. The lesions were divided into four groups - nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT) - for the statistical analysis. The main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by means of cytology or histopathology.