Predicting Extranodal Extension with Preoperative Contrast-enhanced CT in Patients with Oropharyngeal Squamous Cell Carcinoma.

Purpose To develop a practical, easily implementable risk stratification model based on preoperative contrast-enhanced CT (CECT) nodal features to predict the probability of pathologic extranodal extension (pENE) in patients with oropharyngeal squamous cell carcinoma (OPSCC). Materials and Methods Preoperative CECT studies in consecutive patients with OPSCC who underwent surgical resection between October 2012 and October 2020 were examined by four neuroradiologists, blinded to the pathologic outcome, for imaging features of pENE. The pathology report was queried for the presence of pENE. Decision tree analysis with cost-complexity pruning was performed to identify a clinically pragmatic model to predict pENE. Results A total of 162 patients (median age, 60 years [IQR, 54-67 years]; 134 male, 28 female) with 208 dissected heminecks were included. The primary OPSCC site for most patients was tonsil (67%, 109 of 162) or base of tongue (31%, 50 of 162). Most patients had early-stage disease (American Joint Committee on Cancer Staging Manual eighth edition category T0-T2, 93% [151 of 162]; N0-N1, 90% [145 of 162]). Pathologically confirmed pENE was reported in 28% (45 of 162) of patients. CECT features that were significantly associated with pENE on univariable analysis included size, necrosis, spiculation, perinodal stranding, and infiltration of adjacent structures. Decision tree analysis identified a predictive model including spiculation or irregular margins, matted nodes, and infiltration of adjacent structures. The model had a sensitivity of 41% (19 of 46) and specificity of 96% (157 of 162) for predicting pENE. Conclusion The developed model for predicting pENE using preoperative CECT features is practical and had high specificity in patients with OPSCC. Further prospective study is warranted to determine impact on clinical management and outcomes. Head/Neck, CT, Radiation Therapy/Oncology, Neoplasms-Primary, Oncology, Decision Analysis, Observer Performance © RSNA, 2025.