Department of Radiation Oncology, The First Affiliated Hospital of Xi'an Jiao Tong University, Xi'an, Shaan Xi, China.
Department of Thoracic Oncology, West China Hospital, Chengdu, China.
Strahlenther Onkol. 2020 Oct;196(10):913-921. doi: 10.1007/s00066-020-01584-1. Epub 2020 Feb 5.
To extrapolate the infiltration levels of immune cells in patients with lower-grade gliomas (LGGs) using magnetic resonance imaging (MRI)-based radiomic features.
A retrospective dataset of 516 patients with LGGs from The Cancer Genome Atlas (TCGA) database was analysed for the infiltration levels of six types of immune cells using Tumor IMmune Estimation Resource (TIMER) based on RNA sequencing data. Radiomic features were extracted from 107 patients whose pre-operative MRI data are available in The Cancer Imaging Archive; 85 and 22 of these patients were assigned to the training and testing cohort, respectively. The least absolute shrinkage and selection operator (LASSO) was applied to select optimal radiomic features to build the radiomic signatures for extrapolating the infiltration levels of immune cells in the training cohort. The developed radiomic signatures were examined in the testing cohort using Pearson's correlation.
The infiltration levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils and dendritic cells negatively correlated with overall survival in the 516 patient cohort when using univariate Cox's regression. Age, Karnofsky Performance Scale, WHO grade, isocitrate dehydrogenase mutant status and the infiltration of neutrophils correlated with survival using multivariate Cox's regression analysis. The infiltration levels of the 6 cell types could be estimated by radiomic features in the training cohort, and their corresponding radiomic signatures were built. The infiltration levels of B cells, CD8+ T cells, neutrophils and macrophages estimated by radiomics correlated with those estimated by TIMER in the testing cohort. Combining clinical/genomic features with the radiomic signatures only slightly improved the prediction of immune cell infiltrations.
We developed MRI-based radiomic models for extrapolating the infiltration levels of immune cells in LGGs. Our results may have implications for treatment planning.
利用基于磁共振成像（MRI）的放射组学特征推断低级别胶质瘤（LGG）患者的免疫细胞浸润水平。
利用癌症基因组图谱（TCGA）数据库中的 516 例 LGG 患者的回顾性数据集，通过基于 RNA 测序数据的肿瘤免疫估计资源（TIMER）分析六种类型免疫细胞的浸润水平。从癌症成像档案中可获得术前 MRI 数据的 107 例患者中提取放射组学特征；其中 85 例和 22 例患者分别被分配到训练和测试队列。应用最小绝对收缩和选择算子（LASSO）选择最佳放射组学特征，建立训练队列中推断免疫细胞浸润水平的放射组学特征。利用 Pearson 相关性检验在测试队列中检验所开发的放射组学特征。
在 516 例患者队列中，使用单因素 Cox 回归分析时，B 细胞、CD4+T 细胞、CD8+T 细胞、巨噬细胞、中性粒细胞和树突状细胞的浸润水平与总生存率呈负相关。多因素 Cox 回归分析显示，年龄、Karnofsky 表现状态、世界卫生组织分级、异柠檬酸脱氢酶突变状态和中性粒细胞浸润与生存率相关。在训练队列中，可以通过放射组学特征来估计 6 种细胞类型的浸润水平，并建立相应的放射组学特征。在测试队列中，通过放射组学估计的 B 细胞、CD8+T 细胞、中性粒细胞和巨噬细胞的浸润水平与通过 TIMER 估计的浸润水平相关。将临床/基因组特征与放射组学特征相结合，仅略微提高了免疫细胞浸润的预测能力。
我们开发了基于 MRI 的放射组学模型，用于推断 LGG 中免疫细胞的浸润水平。我们的研究结果可能对治疗计划具有启示意义。
