Abstract
Background
The peritumoral region (PTR) of glioblastoma (GBM) appears as a T2W-hyperintensity and is composed of microscopic tumor and edema. Infiltrative low grade glioma (LGG) comprises tumor cells that seem similar to GBM PTR on MRI. The work here explored if a radiomics-based approach can distinguish between the two groups (tumor and edema versus tumor alone).
Methods
Patients with GBM and LGG imaged using a 1.5 T MRI were included in the study. Image data from cases of GBM PTR, and LGG were manually segmented guided by T2W hyperintensity. A set of 91 first-order and texture features were determined from each of T1W-contrast, and T2W-FLAIR, diffusion-weighted imaging sequences. Applying filtration techniques, a total of 3822 features were obtained. Different feature reduction techniques were employed, and a subsequent model was constructed using four machine learning classifiers. Leave-one-out cross-validation was used to assess classifier performance.
Results
The analysis included 42 GBM and 36 LGG. The best performance was obtained using AdaBoost classifier using all the features with a sensitivity, specificity, accuracy, and area of curve (AUC) of 91%, 86%, 89%, and 0.96, respectively. Amongst the feature selection techniques, the recursive feature elimination technique had the best results, with an AUC ranging from 0.87 to 0.92. Evaluation with the F-test resulted in the most consistent feature selection with 3 T1W-contrast texture features chosen in over 90% of instances.
Conclusions
Quantitative analysis of conventional MRI sequences can effectively demarcate GBM PTR from LGG, which is otherwise indistinguishable on visual estimation.
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Data Availability
Data will be made available on request to the corresponding author following institutional ethics committee protocols.
Code availability
The radiomic feature extraction was performed using freely available Pyradiomics software (http://www.pyradiomics.io/pyradiomics.html). All standardization, model fitting, and assessment were performed using Scikit-Learn (https://scikit-learn.org/stable).
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Acknowledgements
We express our sincere gratitude to the patients and their caregivers involved in the study. We would like to thank the Terry Fox Foundation Program Project Grant from the Hecht Foundation for the funding support associated with the study.
Funding
Terry Fox Foundation Program Project Grant from the Hecht Foundation (1083) awarded to Gregory J. Czarnota. The funding bodies had no influence on the study design, data collection, analysis, interpretation of data, or the manuscript's writing.
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Conceptualization: AD, BG, AS, GJC; Methodology: All authors; Formal Analysis and investigation: All authors; Writing-original draft preparation: NM, AD, BG, AS, GJC; Writing-review and editing: All authors; Project administration and supervision: AS, GJC; Funding acquisition: GJC. All the authors are in agreement and accountable for all the aspects of the work.
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Nauman Malik: None. Benjamin Geraghty: None. Archya Dasgupta: None. Pejman Maralani: None. Michael Sandhu: None. Jay Detsky: None. Chia-Lin Tseng: Travel accommodations/expenses & honoraria for past educational seminars by Elekta, belongs to the Elekta MR-Linac Research Consortium, and advisor/consultant with Sanofi. Hany Soliman: None. Sten Myrehaug: Travel accommodations/expenses from Elekta AB. Research support from Novartis/AAA. Zain Husain: Travel accommodations/expenses from Elekta. James Perry: None. Angus Lau: None. Arjun Sahgal: Advisor/consultant with AbbVie, Merck, Roche, Varian (Medical Advisory Group), Elekta (Gamma Knife Icon), BrainLAB, and VieCure (Medical Advisory Board). Board Member: International Stereotactic Radiosurgery Society (ISRS). Past educational seminars with Elekta AB, Accuray Inc., Varian (CNS Teaching Faculty), BrainLAB, Medtronic Kyphon. Research grant with Elekta AB. Travel accommodations/expenses by Elekta, Varian, BrainLAB. Elekta MR Linac Research Consortium, Elekta Spine, Oligometastases and Linac Based SRS Consortia. Gregory J. Czarnota: Funding received from the Terry Fox Foundation Program Project Grant.
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Malik, N., Geraghty, B., Dasgupta, A. et al. MRI radiomics to differentiate between low grade glioma and glioblastoma peritumoral region. J Neurooncol 155, 181–191 (2021). https://doi.org/10.1007/s11060-021-03866-9
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DOI: https://doi.org/10.1007/s11060-021-03866-9