间皮素作为癌症免疫治疗的生物标志物和治疗靶点
Mesothelin as Biomarker and Therapeutic Target for Immunotherapy in Cancer.
癌症仍是一个关键的全球健康问题,原因在于发现晚、耐药和高死亡率。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Association between radiomics features of DCE-MRI and CD8(+) and CD4(+) TILs in advanced gastric cancer.
Association between radiomics features of DCE-MRI and CD8(+) and CD4(+) TILs in advanced gastric cancer.
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本研究旨在探讨晚期胃癌患者肿瘤浸润CD8+和CD4+T细胞水平与动态对比增强磁共振成像(DCE-MRI)定量药代动力学参数之间的相关性。
我们回顾性分析了103例经组织病理学确诊的晚期胃癌(AGC)患者的数据。通过Omni Kinetics软件获得三个药代动力学参数K ep、K trans和V e及其影像组学特征。采用免疫组织化学染色测定CD4+和CD8+TILs。随后进行统计分析,评估影像组学特征与CD4+和CD8+TIL密度之间的相关性。
本研究纳入的所有患者最终分为CD8+TILs低密度组(n=51)(CD8+TILs<138)和高密度组(n=52)(CD8+TILs 138),以及CD4+TILs低密度组(n=51)(CD4+TILs<87)和高密度组(n=52)(CD4+TILs 87)。基于K ep的ClusterShade和Skewness以及基于K trans的Skewness均与CD8+TIL水平呈中度负相关(r=0.630-0.349,p<0.001),其中基于K ep的ClusterShade负相关性最高(r=-0.630,p<0.001)。基于K ep的Inertia与CD4+TIL水平呈中度正相关(r=0.549,p<0.001),基于K ep的Correlation与CD4+TIL水平呈中度负相关,其相关系数也最高(r=-0.616,p<0.001)。通过ROC曲线评估上述特征的诊断效能。对于CD8+TILs,K ep的ClusterShade具有最高的平均曲线下面积(AUC)(0.863)。对于CD4 + TILs,K ep的相关性具有最高的平均AUC(0.856)。
DCE-MRI的影像组学特征与AGC中肿瘤浸润性CD8 + 和CD4 + T细胞的表达相关,这具有无创评估AGC患者中CD8 + 和CD4 + TILs表达的潜力。
Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8 + and CD4 + T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer. Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, K ep , K trans , and V e , and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4 + and CD8 + TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4 + and CD8 + TIL density. Results: All patients included in this study were finally divided into either a CD8 + TILs low-density group ( n = 51) (CD8 + TILs < 138) or a high-density group ( n = 52) (CD8 + TILs 138), and a CD4 + TILs low-density group ( n = 51) (CD4 + TILs < 87) or a high-density group ( n = 52) (CD4 + TILs 87).
ClusterShade and Skewness based on K ep and Skewness based on K trans both showed moderate negative correlation with CD8 + TIL levels ( r = 0. 630-0. 349, p < 0. 001), with ClusterShade based on K ep having the highest negative correlation ( r = -0. 630, p < 0. 001). Inertia-based K ep showed a moderate positive correlation with the CD4 + TIL level ( r = 0. 549, p < 0. 001), and the Correlation based on K ep showed a moderate negative correlation with the CD4 + TIL level, which also had the highest correlation coefficient ( r = -0.
616, p < 0. 001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8 + TILs, ClusterShade of K ep had the highest mean area under the curve (AUC) (0. 863). For CD4 + TILs, the Correlation of K ep had the highest mean AUC (0. 856). Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8 + and CD4 + T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8 + and CD4 + TILs in AGC patients.
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