Advancing Parotid Tumor Diagnosis with KAN Transformer: Precise Differentiation of Pleomorphic Adenoma and Warthin Tumor: A Multicenter Study

Motivation: Parotid gland pleomorphic adenomas (PPAs) and Warthin tumors (PWTs) have overlapping imaging features, making preoperative differentiation challenging. Accurate differentiation is needed to guide individualized treatment and improve patient outcomes. Goal(s): This study aims to develop an AI model for accurate preoperative prediction of parotid PPAs and PWTs. Approach: A parotid tumor segmentation model was developed using the MONAI framework with Unet network, followed by MRI-based predictive models: a radiomics model and deep learning models, Kolmogorov-Arnold Network Transformer (KAT) and Vision Transformer (VIT). Results: The KAT model demonstrated superior accuracy over VIT and radiomics in distinguishing PPAs from PWTs, even with limited imaging data. Impact: The KAT model provides precise early diagnosis of benign parotid tumors, even with limited MRI data. These findings could extend to other tumor types, improving diagnostic accuracy and supporting individualized treatment.