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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
Original Article

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, \(\hbox {T}_{2}\)-weighted images (WI) and post-contrast \(\hbox {T}_{1}\hbox {WI}\). 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 (\(46.2\pm 30.0\) vs. \(30.8\pm 16.8\) 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.

Keywords

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

Notes

Acknowledgements

To Esther Eshkol for editorial assistance.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. For this type of study, formal consent was not required.

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Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.Division of NeurosurgeryTel Aviv Sourasky Medical CenterTel AvivIsrael
  2. 2.Sackler Faculty of MedicineTel Aviv UniversityTel AvivIsrael
  3. 3.The Functional Brain CenterTel Aviv Sourasky Medical CenterTel AvivIsrael
  4. 4.Department of Chemical PhysicsWeizmann Institute of ScienceRehovotIsrael
  5. 5.Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
  6. 6.Neuro-Oncology ServiceTel-Aviv Medical CenterTel AvivIsrael
  7. 7.Department of NeurosurgeryShaare Zedek Medical CenterJerusalemIsrael

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