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Identification of magnetic resonance imaging features for the prediction of molecular profiles of newly diagnosed glioblastoma

Authors
 Sung Soo Ahn  ;  Chansik An  ;  Yae Won Park  ;  Kyunghwa Han  ;  Jong Hee Chang  ;  Se Hoon Kim  ;  Seung-Koo Lee  ;  Soonmee Cha 
Citation
 JOURNAL OF NEURO-ONCOLOGY, Vol.154(1) : 83-92, 2021-08 
Journal Title
JOURNAL OF NEURO-ONCOLOGY
ISSN
 0167-594X 
Issue Date
2021-08
Keywords
Glioblastoma ; Magnetic resonance imaging ; Molecular alterations ; Molecular profiles
Abstract
Purpose: We predicted molecular profiles in newly diagnosed glioblastoma patients using magnetic resonance (MR) imaging features and explored the associations between imaging features and major molecular alterations.

Methods: This retrospective study included patients with newly diagnosed glioblastoma and available next-generation sequencing results. From preoperative MR imaging, Visually AcceSAble Rembrandt Images (VASARI) features, volumetric parameters, and apparent diffusion coefficient (ADC) values were obtained. First, univariate random forest was performed to identify gene abnormalities that could be predicted by imaging features with high accuracy and stability. Next, multivariate random forest was trained to predict the selected genes in the discovery cohort and was validated in the external cohort. Univariable logistic regression was performed to further explore the associations between imaging features and genes.

Results: Univariate random forest identified nine genes predicted by imaging features, with high accuracy and stability. The multivariate random forest model showed excellent performance in predicting IDH and PTPN11 mutations in the discovery cohort, which were validated in the external validation cohorts (areas under the receiver operator characteristic curve [AUCs] of 0.855 for IDH and 0.88 for PTPN11). ATRX loss and EGFR mutation were predicted with AUCs of 0.753 and 0.739, respectively, whereas PTEN could not be reliably predicted. Based on univariable logistic regression analyses, IDH, ATRX, and TP53 were clustered according to their shared imaging features, whereas EGFR and CDKN2A/B were clustered in the opposite direction.

Conclusions: MR imaging features are related to specific molecular alterations and can be used to predict molecular profiles in patients with newly diagnosed glioblastoma.
Full Text
https://link.springer.com/article/10.1007%2Fs11060-021-03801-y
DOI
10.1007/s11060-021-03801-y
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Se Hoon(김세훈) ORCID logo https://orcid.org/0000-0001-7516-7372
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Ahn, Sung Soo(안성수) ORCID logo https://orcid.org/0000-0002-0503-5558
Lee, Seung Koo(이승구) ORCID logo https://orcid.org/0000-0001-5646-4072
Chang, Jong Hee(장종희) ORCID logo https://orcid.org/0000-0003-1509-9800
Han, Kyung Hwa(한경화)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184851
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