Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
Department of Pathology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, People's Republic of China.
J Cancer Res Clin Oncol. 2020 May;146(5):1253-1262. doi: 10.1007/s00432-020-03153-6. Epub 2020 Feb 17.
To evaluate a radiomic approach for the stratification of diffuse gliomas with distinct prognosis and provide additional resolution of their clinicopathological and molecular characteristics.
For this retrospective study, a total of 704 radiomic features were extracted from the multi-channel MRI data of 166 diffuse gliomas. Survival-associated radiomic features were identified and submitted to distinguish glioma subtypes using consensus clustering. Multi-layered molecular data were used to observe the different clinical and molecular characteristics between radiomic subtypes. The relative profiles of an array of immune cell infiltrations were measured gene set variation analysis approach to explore differences in tumor immune microenvironment.
A total of 6 categories, including 318 radiomic features were significantly correlated with the overall survival of glioma patients. Two subgroups with distinct prognosis were separated by consensus clustering of radiomic features that significantly associated with survival. Histological stage and molecular factors, including IDH status and MGMT promoter methylation status were significant differences between the two subtypes. Furthermore, gene functional enrichment analysis and immune infiltration pattern analysis also hinted that the inferior prognosis subtype may more response to immunotherapy.
A radiomic model derived from multi-parameter MRI of the gliomas was successful in the risk stratification of diffuse glioma patients. These data suggested that radiomics provided an alternative approach for survival estimation and may improve clinical decision-making.
评估一种基于放射组学的方法，对具有不同预后的弥漫性神经胶质瘤进行分层，并提供其临床病理和分子特征的额外解析。
在这项回顾性研究中，从 166 例弥漫性神经胶质瘤的多通道 MRI 数据中提取了 704 个放射组学特征。识别与生存相关的放射组学特征，并使用共识聚类对其进行区分，以区分不同的神经胶质瘤亚型。使用多层次的分子数据来观察放射组学亚型之间不同的临床和分子特征。通过基因集变异分析（gene set variation analysis，GSVA）方法测量一系列免疫细胞浸润的相对谱，以探讨肿瘤免疫微环境的差异。
共有 6 个类别，包括 318 个与胶质瘤患者总生存显著相关的放射组学特征。通过对与生存显著相关的放射组学特征进行共识聚类，将弥漫性神经胶质瘤患者分为具有不同预后的 2 个亚组。这两个亚型在组织学分期和分子因素方面存在显著差异，包括 IDH 状态和 MGMT 启动子甲基化状态。此外，基因功能富集分析和免疫浸润模式分析也提示预后较差的亚型可能对免疫治疗更敏感。
从胶质瘤的多参数 MRI 中提取的放射组学模型成功地对弥漫性神经胶质瘤患者进行了风险分层。这些数据表明，放射组学为生存估计提供了一种替代方法，可能有助于改善临床决策。
