Abstract
BackgroundThis 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.MethodsRadiomics 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.ResultsThe 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.ConclusionRadiogenomics 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.
Highlights
Glioblastoma multiforme (GBM), one of the most invasive and fatal brain tumours that develops from glial cells, can severely affects the central nervous system and general health [1]
We aimed to investigate the machine learning based methods in combi‐ nation with radiogenomics to study the associations among MRI fea‐ tures, genomics and the survival rates in glioblastoma multiforme (GBM) patients
We hypothesize that radiomics features of FLAIR imaging data can be predictive of patients’ survival, and radiogenomics analysis can reveal the linkage between images features and known genes in pre‐ viously defined molecular pathways
Summary
Glioblastoma multiforme (GBM), one of the most invasive and fatal brain tumours that develops from glial cells, can severely affects the central nervous system and general health [1]. Due to the heterogeneous nature of GBM, relatively high age of disease onset, migration of malignant cells to surrounding tissue, the treatment outcome for GBM are highly variable, yielding an average survival rate of 12.6 months [2]. Plasma, or cell lines used protein expression data to reveal that common alternations in GBM include mutations of specific gene and proteins such as RTKs, TP53 RB1 and increased expression of EGFR and PDGFRA [4, 5]. The emergence of radiogenomics, combing radio‐ mics image features and genomics, allows the study of GBM more comprehensively. We aimed to investigate the machine learning based methods in combi‐ nation with radiogenomics to study the associations among MRI fea‐ tures, genomics and the survival rates in GBM patients. We hypothesize that radiomics features of FLAIR imaging data can be predictive of patients’ survival, and radiogenomics analysis can reveal the linkage between images features and known genes in pre‐ viously defined molecular pathways
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