Artificial Intelligence Model for Imaging-Based Extranodal Extension Detection and Outcome Prediction in Human Papillomavirus−Positive Oropharyngeal Cancer

Can an artificial intelligence (AI)−driven model predict imaging-based extranodal extension (iENE) and oncologic outcomes from pretreatment computed tomography scans of patients with human papillomavirus (HPV)−positive oropharyngeal squamous cell carcinoma (OPSCC)?In this single-center cohort study of 397 patients with HPV-positive cN OPSCC, an automated pipeline integrating lymph node segmentation and iENE classification achieved an area under the receiver operating characteristic curve of 0.81. AI-predicted iENE was significantly associated with worse distant failure, recurrence-free survival, and overall survival, and outperformed expert radiologist assessment.These findings suggest that automated iENE detection using AI models may offer a powerful prognostic tool to complement clinical decision-making in HPV-positive OPSCC and extend iENE interpretation capabilities to centers that lack specialized radiologists.

Corresponding Author: Laurent Letourneau-Guillon, MD, MSc, Centre Hospitalier de l’Université de Montréal, 1051 Rue Sanguinet, Montréal, QC H2X 3E4, Canada (laurent.letourneau-guillon.1@umontreal.ca).

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