背景:准确的微卫星不稳定性(MSI)测试对于识别符合免疫治疗条件的胃癌(GC)患者至关重要。我们旨在开发和验证基于CT的影像组学特征,以预测GC中的MSI和免疫治疗结果。
方法:这项回顾性多队列研究纳入了来自中国两个独立医疗中心和癌症影像档案(TCIA)数据库的457例GC患者。主要队列(n=201,中心1,2017-2022),通过最小绝对收缩和选择算子(LASSO)和逻辑回归分析用于签名开发。两个独立的免疫治疗队列,一个来自中心1(n=184,2018-2021),另一个来自中心2(n=43,2020-2021),用于评估签名与免疫治疗反应和生存率的关联。使用受试者工作特征曲线下面积(AUC)评估诊断效率,和生存结局通过Kaplan-Meier方法进行分析。纳入TCIA队列(n=29),以使用CT图像和mRNA测序数据评估影像组学特征亚组的免疫浸润情况。
结果:确定了9个影像组学特征用于签名开发,在训练(AUC:0.851,95CI:0.782,0.919)和验证队列(AUC:0.816,95CI:0.706,0.926)中均表现出出色的判别能力。radscore,使用签名计算,在免疫治疗队列中对客观反应表现出很强的预测能力(AUC:0.734,95CI:0.662,0.806;AUC:0.724,95CI:0.572,0.877).此外,radscore与PFS和OS显著相关,radscore较低的GC患者从免疫疗法中获得了显着的生存益处。免疫浸润分析显示CD8+T细胞水平显著升高,激活的CD4+B细胞,和TNFRSF18在低radscore组中的表达,而高radscore组表现出更高水平的T细胞调节性和HHLA2表达。
结论:这项研究开发了一种强大的影像组学特征,有可能作为GC的MSI状态和免疫治疗反应的非侵入性生物标志物,显示与免疫治疗后PFS和OS的显著联系。此外,在低radscore组和高radscore组之间观察到不同的免疫谱,强调其潜在的临床意义。
BACKGROUND: Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC.
METHODS: This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature\'s association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data.
RESULTS: Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression.
CONCLUSIONS: This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC\'s MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.