Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Department of Neurosurgery, Guangzhou General Hospital of Guangzhou Military Command, Guangzhou, China.
Eur Radiol. 2018 Sep;28(9):3640-3650. doi: 10.1007/s00330-017-5302-1. Epub 2018 Mar 21.
To build a reliable radiomics model from multiregional and multiparametric magnetic resonance imaging (MRI) for pretreatment prediction of O-methylguanine-DNA methyltransferase (MGMT) promotor methylation status in glioblastoma multiforme (GBM).
In this retrospective multicentre study, 1,705 multiregional radiomics features were automatically extracted from multiparametric MRI. A radiomics model with a minimal set of all-relevant features and a radiomics model with univariately-predictive and non-redundant features were built for MGMT methylation prediction from a primary cohort (133 patients) and tested on an independent validation cohort (60 patients). Predictive models combing clinical factors were built and evaluated. Both radiomics models were assessed on subgroups stratified by clinical factors.
The radiomics model with six all-relevant features allowed pretreatment prediction of MGMT methylation (AUC=0.88, accuracy=80 %), which significantly outperformed the model with eight univariately-predictive and non-redundant features (AUC=0.76, accuracy=70 %). Combing clinical factors with radiomics features did not benefit the prediction performance. The all-relevant model achieved significantly better performance in stratified analysis.
Radiomics model built from multiregional and multiparameter MRI may serve as a potential imaging biomarker for pretreatment prediction of MGMT methylation in GBM. The all-relevant features have the potential of offering better predictive power than the univariately-predictive and non-redundant features.
• Multiregional and multiparametric MRI features reliably predicted MGMT methylation in multicentre cohorts. • All-relevant imaging features predicted MGMT methylation better than univariately-predictive and non-redundant features. • Combing clinical factors with radiomics features did not benefit the prediction performance.
建立一个基于多区域和多参数磁共振成像（MRI）的可靠放射组学模型，用于预测胶质母细胞瘤（GBM）中 O-甲基鸟嘌呤-DNA 甲基转移酶（MGMT）启动子甲基化状态的预处理。
在这项回顾性多中心研究中，从多参数 MRI 中自动提取了 1705 个多区域放射组学特征。从一个主要队列（133 例患者）中构建了一个具有最小全相关特征集的放射组学模型和一个具有单变量预测和非冗余特征的放射组学模型，并在独立验证队列（60 例患者）中进行了测试。还构建和评估了结合临床因素的预测模型。评估了这两个放射组学模型在按临床因素分层的亚组中的表现。
允许使用六个全相关特征的放射组学模型进行 MGMT 甲基化的预处理预测（AUC=0.88，准确性=80%），明显优于使用八个单变量预测和非冗余特征的模型（AUC=0.76，准确性=70%）。将临床因素与放射组学特征相结合并不能提高预测性能。全相关模型在分层分析中表现出明显更好的性能。
从多区域和多参数 MRI 构建的放射组学模型可能成为 GBM 中 MGMT 甲基化预处理预测的潜在成像生物标志物。全相关特征有可能比单变量预测和非冗余特征提供更好的预测能力。
多区域和多参数 MRI 特征可在多中心队列中可靠地预测 MGMT 甲基化。
全相关影像特征预测 MGMT 甲基化的效果优于单变量预测和非冗余特征。
将临床因素与放射组学特征相结合并不能提高预测性能。
