Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Department of Medical Imaging, The Third People's Hospital of Guizhou Province, Guiyang, Guizhou, China.
J Cell Mol Med. 2019 Jun;23(6):4375-4385. doi: 10.1111/jcmm.14328. Epub 2019 Apr 18.
This study aimed to examine multi-dimensional MRI features' predictability on survival outcome and associations with differentially expressed Genes (RNA Sequencing) in groups of glioblastoma multiforme (GBM) patients.
Radiomics features were extracted from segmented lesions of T2-FLAIR MRI data of 137 GBM patients. Radiomics features include intensity, shape and textural features in seven classes were included in the analysis. Patients were divided into two groups depending on their survival time (shorter or longer than 1-year survival). Four different machine learning algorithms were implemented to construct the prediction models. Features with top importance (importance >0.04) were selected to construct the prediction model using the model with the best performance. The interactions between image features and genomics were then analysed with Pearson's correlation analysis.
The GBDT model with 72 features with highest importance had the highest accuracy of 0.81 on both short and long survival time classes, and the area under the curve (AUC) of the receiver operative characteristic (ROC) of the short and long survival time class were 0.79 and 0.81. Six metagenes showed significant interactive effect (P < 0.05), and Pearson's correlation analysis revealed that three of these metagenes (TIMP1, ROS1 EREG) showed moderate (0.3 < |r| < 0.5) or high correlation (|r| > 0.5) with image features.
Radiogenomics analysis shows that MRI features are predictive of survival outcomes, and image features are highly associated with selective metagenes. Radiogenomics analysis is a useful method for optimizing clinical diagnosis and selecting effective treatments.
本研究旨在探讨多模态 MRI 特征对生存结局的预测能力，以及与胶质母细胞瘤（GBM）患者分组中差异表达基因（RNA 测序）的相关性。
从 137 例 GBM 患者的 T2-FLAIR MRI 数据中提取分割病变的放射组学特征。放射组学特征包括强度、形状和纹理特征，共分为七类。根据患者的生存时间（生存时间是否超过 1 年）将患者分为两组。实施了四种不同的机器学习算法来构建预测模型。选择具有最高重要性（重要性>0.04）的特征，使用性能最佳的模型构建预测模型。然后使用 Pearson 相关分析分析图像特征与基因组学之间的相互作用。
具有最高重要性（重要性>0.04）的 72 个特征的 GBDT 模型在短期和长期生存时间类中均具有最高的准确性（0.81），短期和长期生存时间类的接收器工作特征（ROC）曲线下面积（AUC）分别为 0.79 和 0.81。六个元基因显示出显著的交互作用（P<0.05），Pearson 相关分析显示其中三个元基因（TIMP1、ROS1、EREG）与图像特征呈中度（0.3<|r|<0.5）或高度相关（|r|>0.5）。
放射组学分析表明 MRI 特征可预测生存结局，图像特征与特定的元基因高度相关。放射组学分析是优化临床诊断和选择有效治疗方法的有用方法。
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