68 Ga-NODAGA-SNA006 PET/CT Imaging of Tumor CD8 + T-Cell Infiltration Predicts Immunotherapy Response in Lung Cancer.

Zhou, Jinxin; Zhou, Min; Chen, Wei; Liu, Yang; Hai, Wangxi; Huang, Jingwen; Wang, Xiaofei; Zhang, Yifan et al. · Clin Nucl Med · 2025

prospective_cohort · Level II

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Abstract

This study aimed to evaluate the potential of 68 Ga-NODAGA-SNA006 PET/CT imaging as a noninvasive method for assessing immune cell infiltration and predicting treatment response in lung cancer patients undergoing immunotherapy. A prospective study enrolled 8 patients with histologically confirmed lung cancer who received first-line chemotherapy combined with anti-PD-1/PD-L1 immunotherapy. 68 Ga-SNA006 PET/CT imaging was performed before treatment. The primary endpoints analyzed the correlation between tumor SUVmax and CD8 + T-cell infiltration, as well as associations of multiparametric imaging parameters (SUVmax, TBR, TLR, TSR, and TMR) with treatment outcomes. The SUVmax significantly correlated with stromal CD8 + T-cell infiltration (R 2 = 0.8218, P = 0.0338) but not with intratumoral infiltration (R 2 = 0.5178, P > 0.05), indicating its stromal-specific association with immune activity. Among 8 patients, 6 achieved partial response and 2 stable diseases after 2 treatment cycles. Baseline blood CD8 + T cells, Ki67%, and PD-L1% showed no prognostic significance, nor did SUVmax correlate with posttreatment lesion reduction. In 24 lesions analyzed, TBR demonstrated the strongest correlation with lesion reduction (R 2 = 0.3178, P < 0.01). ROC analysis further revealed that TBR had the highest diagnostic efficacy (AUC = 0.87) for predicting treatment response, with a sensitivity of 62.5% and specificity of 93.3% at a cutoff value of TBR >2.120, and a sensitivity of 87.5% and specificity of 66.7% at TBR >5.535. 68 Ga-NODAGA-SNA006 PET/CT provides valuable insights into immune cell infiltration and treatment response in lung cancer patients. The study highlights the potential of TBR as a prognostic biomarker and underscores the importance of integrating multiparametric imaging into clinical decision-making.

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