PT - JOURNAL ARTICLE AU - Jianyuan Zhou AU - Sijuan Zou AU - Siyuan Cheng AU - Dong Kuang AU - Dan Li AU - Lixing Chen AU - Cong Liu AU - Jianhua Yan AU - Xiaohua Zhu TI - <strong><strong>Correlation between dual-time-point FDG PET and tumor microenvironment immune types in non-small cell lung cancer</strong><strong>(NSCLC)</strong></strong> DP - 2020 May 01 TA - Journal of Nuclear Medicine PG - 299--299 VI - 61 IP - supplement 1 4099 - http://jnm.snmjournals.org/content/61/supplement_1/299.short 4100 - http://jnm.snmjournals.org/content/61/supplement_1/299.full SO - J Nucl Med2020 May 01; 61 AB - 299Purpose: Dual-time-point (DTP) 18F-FDG PET, which reflects the dynamics of tumor glucose metabolism, may provide a way to characterize tumor and immune cells within tumor immune microenvironment (TIME). We investigated the correlations of metabolic parameters (MPs) on DTP 18F-FDG PET images with tumor microenvironment immune types (TMIT) in patients with NSCLC. Methods: A retrospective analysis was performed in 91 patients with NSCLC who underwent preoperative DTP 18F-FDG PET/CT scans. MPs in the early scan (eSUVmax, eSUVmean, eMTV, eTLG) and delayed scan (dSUVmax, dSUVmean, dMTV, dTLG) were calculated, respectively. The change in MPs (ΔSUVmax, ΔSUVmean, ΔMTV, ΔTLG) between the two time points were calculated. Tumor specimens were analyzed by immunohistochemistry for PD-L1, PD-1 expression and CD8+ TILs. TIME was classified into four immune types (TMIT I ~ IV) according to the expression of PD-L1 and CD8+ TILs. Correlations between MPs with TMITs and the immune markers were analyzed. A composite metabolic signature (Meta-Sig) and a combined model with Meta-Sig and clinical factors were constructed to predict the presence of TMIT I tumors. Results: eSUVmax, eSUVmean, dSUVmax, dSUVmean, ΔSUVmax, ΔSUVmean, and ΔTLG were significantly higher in PD-L1 positive patients (p = 0.0007, 0.0006, &lt; 0.0001, &lt; 0.0001, 0.0002, 0.0002, 0.0247, respectively), and in TMIT-I tumors (p = 0.0001, &lt; 0.0001, &lt; 0.0001, &lt;0.0001, 0.0009, 0.0009, 0.0144, respectively). Compared to stand-alone MP, the Meta-Sig and combined Model displayed better performance for assessing TMIT-I tumors (Meta-sig: AUC = 0.818, sensitivity = 86.36%, specificity = 73.91%; Model: AUC = 0.869, sensitivity = 77.27%, specificity = 82.61%). Conclusions: High glucose metabolism on DTP 18F-FDG PET is relevant to TMIT-I tumors, and the Meta-Sig and combined model based on clinical and metabolic information could improve the performance of identifying the patients who may respond to immunotherapy.