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Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic Significance

1
Department of Neurosurgery, Sungkyunkwan University, School of Medicine, Samsung Medical Center, Seoul 06351, Korea
2
Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea
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Centerfor Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, Korea
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Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul 06351, Korea
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Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
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Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
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Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, 1090 Vienna, Austria
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Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
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Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea
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Department of Biomedical Sciences, Korea University College of Medicine, Seoul 02841, Korea
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School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Cancers 2020, 12(7), 1707; https://doi.org/10.3390/cancers12071707 (registering DOI)
Received: 29 May 2020 / Revised: 22 June 2020 / Accepted: 24 June 2020 / Published: 27 June 2020
(This article belongs to the Section Cancer Informatics and Big Data)
We aimed to evaluate the potential of radiomics as an imaging biomarker for glioblastoma (GBM) patients and explore the molecular rationale behind radiomics using a radio-genomics approach. A total of 144 primary GBM patients were included in this study (training cohort). Using multi-parametric MR images, radiomics features were extracted from multi-habitats of the tumor. We applied Cox-LASSO algorithm to build a survival prediction model, which we validated using an independent validation cohort. GBM patients were consensus clustered to reveal inherent phenotypic subtypes. GBM patients were successfully stratified by the radiomics risk score, a weighted sum of radiomics features, corroborating the potential of radiomics as a prognostic biomarker. Using consensus clustering, we identified three distinct subtypes which significantly differed in the prognosis (“heterogenous enhancing”, “rim-enhancing necrotic”, and “cystic” subtypes). Transcriptomic traits enriched in individual subtypes were in accordance with imaging phenotypes summarized by radiomics. For example, rim-enhancing necrotic subtype was well described by radiomics profiling (T2 autocorrelation and flat shape) and highlighted by the inflammatory genomic signatures, which well correlated to its phenotypic peculiarity (necrosis). This study showed that imaging subtypes derived from radiomics successfully recapitulated the genomic underpinnings of GBMs and thereby confirmed the feasibility of radiomics as an imaging biomarker for GBM patients with comprehensible biologic annotation. View Full-Text
Keywords: glioblastoma; radiomics; biomarker; radiogenomics glioblastoma; radiomics; biomarker; radiogenomics
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Choi, S.W.; Cho, H.-H.; Koo, H.; Cho, K.R.; Nenning, K.-H.; Langs, G.; Furtner, J.; Baumann, B.; Woehrer, A.; Cho, H.J.; Sa, J.K.; Kong, D.-S.; Seol, H.J.; Lee, J.-I.; Nam, D.-H.; Park, H. Multi-Habitat Radiomics Unravels Distinct Phenotypic Subtypes of Glioblastoma with Clinical and Genomic Significance. Cancers 2020, 12, 1707.

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