Simple SummaryThe presence of extranodal extension is a significant prognostic factor in oropharyngeal squamous cell carcinoma. Despite its significance as a prognostic factor, the predictive efficacy of preoperative ENE outcomes falls short of clinical expectations. The aim of this study was to examine the nodal margin-related feature and nodal tumor burden-related feature to enhance the predictive power of preoperative ENE. The study revealed that the inclusion of both margin and burden-related features resulted in higher predictive power compared with the use of either feature alone in predicting ENE. It would be beneficial to incorporate nodal tumor burden assessment in addition to nodal margin evaluation to enhance the accuracy of ENE prediction.AbstractThe importance of risk stratification in the management of oropharyngeal squamous cell carcinoma (OPSCC) is becoming increasingly obvious with the growing evidence of its variable prognosis. We identified and evaluated imaging characteristics predictive of extranodal extension (ENE) in OPSCC. Preoperative computed tomography and histopathologic results of 108 OPSCC patients who underwent neck dissection as primary treatment were analyzed. Imaging characteristics were reassessed for factors associated with nodal margin breakdown and metastatic burden. Moreover, the predictability of pathological ENE (pENE) was analyzed. Univariate and multivariate binomial logistic regression analyses were performed to examine the predictive power of ENE-related radiologic features. Imaging-based characteristics showed variable degrees of association with pENE. Factors associated with nodal margin breakdown (indistinct capsular contour, irregular margin, and perinodal fat stranding) and factors associated with nodal burden (nodal matting, lower neck metastasis, and presence of >4 lymph node metastases) were significantly predictive of ENE (odds ratio (OR) = 11.170 and 12.121, respectively). The combined utilization of the nodal margin and burden factors further increased the predictive ability (OR = 14.710). Factors associated with nodal margin breakdown and nodal burden were associated with pENE, demonstrating the use of combinatorial analysis for more accurate ENE prediction.