Open AccessMachine‐learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time
Author(s) -
Liao Xin,
Cai Bo,
Tian Bin,
Luo Yilin,
Song Wen,
Li Yinglong
Publication year - 2019
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.14328
Subject(s) - radiogenomics , medicine , glioblastoma , receiver operating characteristic , survival analysis , correlation , radiomics , proportional hazards model , oncology , overall survival , artificial intelligence , machine learning , radiology , computer science , cancer research , mathematics , geometry
Background 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. Methods 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. Results 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 ( TIMP 1 , ROS 1 EREG ) showed moderate (0.3 < | r | < 0.5) or high correlation (| r | > 0.5) with image features. Conclusion 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.