ESTRO 2025 - Abstract Book

S3364

Physics - Machine learning models and clinical applications

ESTRO 2025

325

Digital Poster ANALYZING LINGUAL MUSCLE COMPOSITION WITH ARTIFICIAL INTELLIGENCE (IA): AN INNOVATIVE APPROACH TO DYSPHAGIA ASSESSMENT IN HEAD AND NECK CANCER Laura Ferrera-Alayon 1 , Bárbara Salas-Salas 1 , Fiorella Ximena Palmas-Candia 2 , Raquel Cabrera Diaz-Saavedra 1 , Anaïs Ramos Ortiz 1 , Pedro Carlos Lara Jimenez 3,4 , Marta Lloret Sáez-Bravo 1 1 Radiation Oncology, Universitary Hospital Dr Negrín Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain. 2 Endocrinology and Nutrition Department, Vall D’Hebron University Hospital, Barcelona, Spain. 3 Radiation Oncology, Universitary Hospital San Roque, Las Palmas de Gran Canaria, Spain. 4 Radiation Oncology, Las Palmas de Gran Canaria University, Las Palmas de Gran Canaria, Spain Purpose/Objective: Oropharyngeal dysphagia is a common and debilitating condition in head and neck cancer (HNC) patients. This study aimed to evaluate the relationship between tongue muscle composition (quantity and quality) and the risk of dysphagia in non-surgically treated HNC patients, using artificial intelligence (AI) analysis of pretreatment computed tomography (CT) scans Material/Methods: A prospective analysis was conducted on 41 non-surgically treated HNC patients undergoing curative radiotherapy. Tongue muscle quantity was measured as cross-sectional area (cm²) and as a percentage of body composition using AI-based segmentation of CT images. Muscle quality was assessed through Hounsfield Units (HU), representing muscle density. Dysphagia risk was evaluated with the validated EAT- 10 questionnaire, considering scores ≥ 3 as indicative of increased risk. Results: A significant positive correlation was found between tongue muscle area and density (R = 0.230, p = 0.002). However, no significant correlation was observed between the percentage of tongue muscle and density (R = 0.292, p = 0.064)Figure 1. Patients with EAT- 10 scores ≥ 3 had significantly larger percentages of tongue muscle area (mean 61.17 ± 10.44 cm²) compared to those with EAT-10 < 3 (mean 56.58 ± 5.77 cm²; p = 0.004). Additionally, higher tongue muscle density (HU) was associated with increased dysphagia risk (p = 0.046). A significant association was also observed between pre-treatment and post-treatment dysphagia, with patients who reported pre-treatment dysphagia (EAT- 10 ≥ 3) continuing to experience higher post -treatment dysphagia (p = 0.009, R = 0.411). Biologically Effective Dose (BED), advanced tumor stage, and systemic treatment were further associated with increased post treatment dysphagia risk (p = 0.004 and p = 0.027, respectively).

Figure 1.Correlation Between Tongue Muscle Area (cm²) and Quality (HU).