LAUSR

Simple Summary The N staging system for Nasopharyngeal Carcinoma (NPC) is constantly improving for better survival risk stratification with accumulating clinical evidence. Discovering new prognostic factors often depends on clinical observations, which often lack comprehensiveness and precision. This study aimed to propose new quantitative spatial characterizations of LN tumor and demonstrate their feasibility of improving N stage. Independent anatomical prognostic factors were discovered and achieved superior risk stratification performance when combined with N stage. This quantitative approach could be applied to other cancer sites to discover new prognostic or predictive factors and ultimately benefit precision medicine. Abstract This study aims to investigate the feasibility of improving the prognosis stratification of the N staging system of Nasopharyngeal Carcinoma (NPC) from quantitative spatial characterizations of metastatic lymph node (LN) for NPC in a multi-institutional setting. A total of 194 and 284 NPC patients were included from two local hospitals as the discovery and validation cohort. Spatial relationships between LN and the surrounding organs were quantified by both distance and angle histograms, followed by principal component analysis. Independent prognostic factors were identified and combined with the N stage into a new prognostic index by univariate and multivariate Cox regressions on disease-free survival (DFS). The new three-class risk stratification based on the constructed prognostic index demonstrated superior cross-institutional performance in DFS. The hazard ratios of the high-risk to low-risk group were 9.07 (p < 0.001) and 4.02 (p < 0.001) on training and validation, respectively, compared with 5.19 (p < 0.001) and 1.82 (p = 0.171) of N3 to N1. Our spatial characterizations of lymph node tumor anatomy improved the existing N-stage in NPC prognosis. Our quantitative approach may facilitate the discovery of new anatomical characteristics to improve patient staging in other diseases.