Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI

Nguyen Quoc Khanh Le, Truong Nguyen Khanh Hung, Duyen Thi Do, Luu Ho Thanh Lam, Luong Huu Dang, Tuan Tu Huynh

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background: In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients. Methods: This retrospective study retrieved patients from TCGA-GBM and IvyGAP cohorts with pathologically diagnosed glioblastoma, and separated them into different transcriptome subtypes groups. GBM patients were then segmented into three different regions of MRI: enhancement of the tumor core (ET), non-enhancing portion of the tumor core (NET), and peritumoral edema (ED). We subsequently used handcrafted radiomics features (n = 704) from multimodality MRI and two-level feature selection techniques (Spearman correlation and F-score tests) in order to find the features that could be relevant. Results: After the feature selection approach, we identified 13 radiomics features that were the most meaningful ones that can be used to reach the optimal results. With these features, our XGBoost model reached the predictive accuracies of 70.9%, 73.3%, 88.4%, and 88.4% for classical, mesenchymal, neural, and proneural subtypes, respectively. Our model performance has been improved in comparison with the other models as well as previous works on the same dataset. Conclusion: The use of XGBoost and two-level feature selection analysis (Spearman correlation and F-score) could be expected as a potential combination for classifying transcriptome subtypes with high performance and might raise public attention for further research on radiomics-based GBM models.

Original languageEnglish
Article number104320
JournalComputers in Biology and Medicine
Volume132
DOIs
Publication statusPublished - May 2021

Keywords

  • Artificial intelligence
  • Glioblastoma
  • Magnetic resonance imaging
  • Neuroimaging
  • Radiogenomics
  • Radiomics biomarker
  • Transcriptome subtypes
  • XGBoost

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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