Resumen
The management of salivary gland tumors (SGTs), especially their early diagnosis, remains a challenge for physicians. Indeed, differentiating benign and malignant SGTs is an essential step in choosing an appropriate surgical approach. The aim of this study was to increase the effectiveness of pre-surgical diagnosis through a machine learning (ML) diagnostic tool that evaluates inflammatory biomarkers and radiomic metrics extracted from magnetic resonance imaging (MRI) sequences. Specifically, we considered the following indices of inflammation as inflammatory biomarkers: the systemic immune-inflammation index (SII), the systemic inflammation response index (SIRI), the platelet-to-lymphocyte ratio (PLR), and the neutrophil-to-lymphocyte ratio (NLR). In the context of cancer research, however, radiomics enables high-performance quantitative analysis of radiological images. We concluded that inflammatory biomarkers and radiomic features are comparably capable of supporting a differential diagnosis and are easily obtained through the preclinical investigations of patients.