Automated Lymph Node and Extranodal Extension Assessment Improves Risk Stratification in Oropharyngeal Carcinoma

PURPOSE: Extranodal extension (ENE) is a biomarker in oropharyngeal carcinoma (OPC) but can only be diagnosed via surgical pathology. We applied an automated artificial intelligence (AI) imaging platform integrating lymph node autosegmentation with ENE prediction to determine the prognostic value of the number of predicted ENE nodes. MATERIALS AND METHODS: We conducted a multisite, retrospective study of 1,733 OPC patients with pretreatment computed tomography who underwent definitive radiation therapy across three institutions. Malignant lymph nodes were segmented using a validated deep learning auto-segmentation model, and segmented lymph nodes were sequentially processed with a validated ENE prediction model to calculate number of nodes with AI-predicted ENE (AI-ENE) per patient. We evaluated associations of AI-ENE with disease outcomes using site-stratified, multivariable Cox regression, adjusting for human papillomavirus (HPV) status, smoking pack-years, tumor and nodal stage, age, and sex. We evaluated risk-stratification improvement when incorporating AI-ENE into the Radiation Therapy Oncology Group (RTOG)-0129 risk groupings and derived American Joint Committee on Cancer (AJCC) 8th edition staging with Uno C-indices and decision curve analyses. RESULTS: < .001 for each). The largest improvements were observed among HPV-negative patients (C-index: +15% for OS, +14% for DC). CONCLUSION: Automated, AI-ENE node number is a novel risk factor for OPC that may better inform pretreatment risk stratification and decision-making.