Gao Xujie, Ma Tingting, Bai Shuai, Liu Ying, Zhang Yuwei, Wu Yupeng, Li Hui, Ye Zhaoxiang
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
National Clinical Research Center for Cancer, Tianjin 300060, China.
Ann Transl Med. 2020 Apr;8(7):469. doi: 10.21037/atm.2020.03.114.
Tumor infiltrating regulatory T (TITreg) cells are highly infiltrated in gastric cancer (GC) and associated with worse prognosis of GC patients. We aim to develop and validate a radiomics signature for evaluation of TITreg cells and outcome prediction of GC patients.
A total of 165 GC patients from three independent cohorts were enrolled in this retrospective study. The abundance of TITreg cells were evaluated by using multispectral immunohistochemical analysis and CIBERSORT algorithm. The radiomics features were extracted by using PyRadiomics software and the radiomics signature was generated by using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The receiver operator characteristic (ROC) curves were applied to assess the performance of radiomics signature for estimating TITreg cells. Univariable and multivariable Cox regression analysis were used for identifying risk factor of overall survival (OS). The prognostic value of the radiomics signature and the TITreg cells were evaluated by using the Kaplan-Meier method and log-rank test.
Six robust features were selected for building the radiomics signature. The radiomics signature showed good ability for estimating TITreg in the training, validation and testing cohort, with area under the curve (AUC) of 0.884, 0.869 and 0.847, respectively. Multivariable Cox regression analysis showed that the radiomics signature was an independent risk factor of unfavorable OS of GC patients.
The proposed CT-based radiomics signature is a promising non-invasive biomarker of TITreg cells and outcome prediction of GC patients.
肿瘤浸润调节性T（TITreg）细胞在胃癌（GC）中高度浸润，并与GC患者的不良预后相关。我们旨在开发并验证一种用于评估TITreg细胞和预测GC患者预后的放射组学特征。
本回顾性研究纳入了来自三个独立队列的165例GC患者。通过多光谱免疫组化分析和CIBERSORT算法评估TITreg细胞的丰度。使用PyRadiomics软件提取放射组学特征，并使用最小绝对收缩和选择算子（LASSO）逻辑回归模型生成放射组学特征。应用受试者工作特征（ROC）曲线评估放射组学特征估计TITreg细胞的性能。采用单变量和多变量Cox回归分析确定总生存期（OS）的危险因素。使用Kaplan-Meier法和对数秩检验评估放射组学特征和TITreg细胞的预后价值。
选择了六个稳健的特征来构建放射组学特征。放射组学特征在训练、验证和测试队列中显示出良好的估计TITreg细胞的能力，曲线下面积（AUC）分别为0.884、0.869和0.847。多变量Cox回归分析表明，放射组学特征是GC患者不良OS的独立危险因素。
所提出的基于CT的放射组学特征是一种有前景的TITreg细胞非侵入性生物标志物和GC患者预后预测指标。
