医学
肺癌
CD8型
免疫系统
免疫疗法
渗透(HVAC)
PET-CT
肺
核医学
病理
内科学
正电子发射断层摄影术
免疫学
物理
热力学
作者
Jinxin Zhou,Min Zhou,Wei Chen,Yang Liu,Wangxi Hai,Jingwen Huang,Xiaofei Wang,Yifan Zhang,Qiurui Zhang
标识
DOI:10.1097/rlu.0000000000006098
摘要
Background: 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. Patients and Methods: 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. Results: 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. Conclusions: 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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