MRI radiomics analysis of molecular alterations in low-grade gliomas

Ben Shofty, Moran Artzi, Dafna Ben Bashat, Gilad Liberman, Oz Haim, Alon Kashanian, Felix Bokstein, Deborah T. Blumenthal, Zvi Ram, Tal Shahar

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Purpose: Low-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tumor sampling via neurosurgical procedures. The aim of this study was to evaluate the radiomics approach for noninvasive classification of patients with LGG and IDH mutation, based on their 1p/19q codeletion status, by testing different classifiers and assessing the contribution of the different MR contrasts. Methods: Preoperative MRI scans of 47 patients diagnosed with LGG with IDH1-mutated tumors and a genetic analysis for 1p/19q deletion status were included in this study. A total of 152 features, including size, location and texture, were extracted from fluid-attenuated inversion recovery images, T 2-weighted images (WI) and post-contrast T 1WI. Classification was performed using 17 machine learning classifiers. Results were evaluated by a fivefold cross-validation analysis. Results: Radiomic analysis differentiated tumors with 1p/19q intact (n= 21 ; astrocytomas) from those with 1p/19q codeleted (n= 26 ; oligodendrogliomas). Best classification was obtained using the Ensemble Bagged Trees classifier, with sensitivity = 92%, specificity = 83% and accuracy = 87%, and with area under the curve = 0.87. Tumors with 1p/19q intact were larger than those with 1p/19q codeleted (46.2 ± 30.0 vs. 30.8 ± 16.8 cc, respectively; p= 0.03) and predominantly located to the left insula (p= 0.04 ). Conclusion: The proposed method yielded good discrimination between LGG with and without 1p/19q codeletion. Results from this study demonstrate the great potential of this method to aid decision-making in the clinical management of patients with LGG.

Original languageEnglish
Pages (from-to)563-571
Number of pages9
JournalInternational journal of computer assisted radiology and surgery
Volume13
Issue number4
DOIs
StatePublished - 1 Apr 2018
Externally publishedYes

Keywords

  • 1p/19q Codeletion
  • Low-grade gliomas
  • Machine learning classifiers
  • MRI
  • Radiomics

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Health Informatics
  • Computer Graphics and Computer-Aided Design

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