Abstract
BACKGROUND: Metastatic lymph nodes (mLNs) exhibit different responses to neoadjuvant immunotherapy compared to the primary tumor (PT) in non-small cell lung cancer (NSCLC). Evaluating mLNs' response is crucial for predicting treatment efficacy and prognosis; however, such assessments are currently insufficient. METHODS: We enrolled 101 NSCLC patients with mLNs who underwent neoadjuvant chemoimmunotherapy followed by surgery. Survival outcomes and radiological and metabolic changes were analyzed across different lymph node pathological response groups, and a least absolute shrinkage and selection operator (LASSO) logistic regression model was developed to predict mLNs' response. RNA sequencing was performed to characterize the immune microenvironment of lymph nodes with different pathological responses. RESULTS: Residual tumors in mLNs were significantly associated with worse recurrence-free survival (p = 0.003) and a trend toward worse overall survival (p = 0.087). Combining the pathological responses of mLNs and PTs improved prognostic stratification. Neither radiological size changes (AUC: 0.621) nor the SUVmax reduction rate (AUC: 0.645) were effective in distinguishing mLNs response. A model combining radiological and metabolic parameters demonstrated fair prediction efficacy (AUC: 0.85). In separate analyses of N1 and N2 nodes, radiological and metabolic changes of N1 mLNs partly reflected their pathologic response (AUC: 0.734; 0.816), unlike in N2 mLNs. RNA sequencing revealed that immune infiltration in responding lymph nodes differed from non-responding ones, with higher CD8+ T cells, NK T cells, B cells, and dendritic cells in the former. CONCLUSION: The pathological response of mLNs provides additional prognostic information, but current tools are ineffective for detecting residual tumors. A model integrating radiological and metabolic parameters may offer better prediction.