Machine‐learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time

Abstract

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.

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

“…Using 84 patients’ TCGA data as validation cohort (and Chinese Glioma Genome Atlas—CGGA as training set), the same group [29] have performed a radiogenomic analysis showing the ability of radiomic features to predict progression-free survival and their association with the immune response, programmed cell death, cell proliferation, and vasculature development, as reported by transcriptomic data. Liao et al [30], using a machine learning approach on data from 46 patients, showed high or moderate correlations between differential expression of three patterns of gene expression—also defined metagenes ( TIMP1 , ROS1 , EREG ) and image features. In particular, EREG was found positively associated with dependence non-uniformity, difference average, contrast, and cluster prominence.…”

Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques

“…It is able to transform the images into analyzable statistics with mathematical calculation, which could be further applied in machine learning technology. This set of methods has been widely explored in clinical diagnosis, tumor grading, treatment prediction, and survival prediction by previous researchers (16,(22)(23)(24)(25)(26)(27). For example, the study applying RF classifier in the discrimination between primary central nervous system lymphoma and atypical glioblastoma represented satisfactory diagnostic ability with AUC of 0.921 (27).…”

MRI Radiomic Features to Predict IDH1 Mutation Status in Gliomas: A Machine Learning Approach using Gradient Tree Boosting

“…Radiomic features were calculated from the VOIs using Olea Sphere software. A total of 92 texture features were collected: 19 first-order metrics, including the mean, standard deviation, skewness, and kurtosis, and 73 second-order metrics consisting of 23 gray level run length matrix [41], 16 gray level run length matrix [42], 15 gray level size zone matrix [43], five neighboring gray tone difference matrix [44], and 14 gray level dependence matrix [45]. Definitions and calculations of these features are explained elsewhere [46].…”