Improving prediction of lymph node response to neoadjuvant chemoimmunotherapy in resectable non-small cell lung cancer via dynamic [¹⁸F]FDG PET/CT.

Abstract

Accurately predicting the pathological response of lymph nodes to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) remains a challenge. This study aimed to evaluate the effectiveness of [¹⁸F]FDG PET/CT imaging in predicting lymph node response to neoadjuvant chemoimmunotherapy in resectable NSCLC. We investigated the predictive value of dynamic changes in imaging features of both primary tumors and lymph nodes. A total of 86 patients with stage IIB-IIIB NSCLC were enrolled. [¹⁸F]FDG PET/CT scans were conducted at baseline and after neoadjuvant chemoimmunotherapy, but before surgery. SUVmax and size of primary tumors and lymph nodes were measured, and their dynamic changes were analyzed to correlate with nodal response. The predictive accuracy was assessed using the area under the receiver operating characteristic curve. Event-free survival (EFS) was evaluated using Kaplan-Meier analysis. The metabolism composite index, which combines changes in SUVmax of the primary tumor and post-treatment SUVmax of lymph nodes, significantly improved lymph node response prediction, with an AUC of 0.852 (95% CI: 0.772, 0.932), sensitivity of 0.783, and specificity of 0.811. Patients without a pathological complete response (pCR) had shorter median EFS in the non-pCR/PET (N+) group compared to the non-pCR/PET (N-) group (17 months vs 42 months, p < 0.001). Integrating dynamic metabolic changes in the primary tumor with post-treatment lymph node metabolic status is highly effective in predicting lymph node response to neoadjuvant chemoimmunotherapy. This method also helps identify patients with less favorable prognoses within the non-pCR group. Question How effective is [¹⁸F]FDG PET/CT in predicting lymph node response to neoadjuvant chemoimmunotherapy in resectable NSCLC through dynamic changes in metabolic features? Findings The integration of SUVmax changes in the primary tumor with post-treatment SUVmax of lymph nodes achieved a predictive accuracy characterized by an AUC of 0.852. Clinical relevance The use of dynamic metabolic changes from [¹⁸F]FDG PET/CT enhances the prediction of lymph node response and helps identify NSCLC patients with poor prognosis, allowing for improved risk stratification and targeted postoperative interventions.