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

Nguyen Quoc Khanh Le1, Truong Nguyen Khanh Hung2, Duyen Thi Do3

  • 1Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan.

Computers in Biology and Medicine
|March 18, 2021
PubMed
Summary

This study introduces an eXtreme Gradient Boosting (XGBoost) radiomics model to classify glioblastoma transcriptome subtypes, offering improved accuracy over previous methods for better patient treatment.

Keywords:
Artificial intelligenceGlioblastomaMagnetic resonance imagingNeuroimagingRadiogenomicsRadiomics biomarkerTranscriptome subtypesXGBoost

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Area of Science:

  • Neuro-oncology
  • Radiomics
  • Machine Learning in Medicine

Background:

  • Glioma transcriptome subtypes are crucial biomarkers for diagnosis and prognosis.
  • Current identification methods face limitations like long detection times and biopsy challenges.
  • Intratumoral heterogeneity in glioblastoma complicates subtype classification.

Purpose of the Study:

  • To evaluate an eXtreme Gradient Boosting (XGBoost)-based radiomics model for classifying glioblastoma (GBM) transcriptome subtypes.
  • To overcome limitations of existing transcriptome subtype identification methods.
  • To improve diagnostic and prognostic accuracy for GBM patients.

Main Methods:

  • Retrospective analysis of TCGA-GBM and IvyGAP glioblastoma cohorts.
  • Segmentation of MRI scans into enhancement of the tumor core (ET), non-enhancing tumor core (NET), and peritumoral edema (ED).
  • Application of 704 handcrafted radiomics features and two-level feature selection (Spearman correlation, F-score tests).

Main Results:

  • Identified 13 optimal radiomics features for classification.
  • Achieved high predictive accuracies for transcriptome subtypes: Classical (70.9%), Mesenchymal (73.3%), Neural (88.4%), and Proneural (88.4%).
  • Demonstrated improved model performance compared to existing methods on the same dataset.

Conclusions:

  • The XGBoost radiomics model with feature selection shows high potential for classifying glioblastoma transcriptome subtypes.
  • This approach offers a promising, non-invasive method for subtype identification.
  • Further research into radiomics-based GBM models is warranted.