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MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas

Authors
 C J Park  ;  K Han  ;  H Kim  ;  S S Ahn  ;  D Choi  ;  Y W Park  ;  J H Chang  ;  S H Kim  ;  S Cha  ;  S-K Lee 
Citation
 AMERICAN JOURNAL OF NEURORADIOLOGY, Vol.42(3) : 448-456, 2021-03 
Journal Title
AMERICAN JOURNAL OF NEURORADIOLOGY
ISSN
 0195-6108 
Issue Date
2021-03
MeSH
Adult ; Brain Neoplasms / diagnostic imaging* ; Brain Neoplasms / genetics* ; Female ; Glioblastoma / diagnostic imaging* ; Glioblastoma / genetics* ; Humans ; Image Interpretation, Computer-Assisted ; Isocitrate Dehydrogenase / genetics ; Machine Learning ; Magnetic Resonance Imaging / methods* ; Male ; Middle Aged ; Mutation ; Retrospective Studies
Abstract
Background and purpose: Isocitrate dehydrogenase (IDH) wild-type lower-grade gliomas (histologic grades II and III) with epidermal growth factor receptor (EGFR) amplification or telomerase reverse transcriptase (TERT) promoter mutation are reported to behave similar to glioblastoma. We aimed to evaluate whether MR imaging features could identify a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma.

Materials and methods: In this multi-institutional retrospective study, pathologically confirmed IDH wild-type lower-grade gliomas from 2 tertiary institutions and The Cancer Genome Atlas constituted the training set (institution 1 and The Cancer Genome Atlas, 64 patients) and the independent test set (institution 2, 57 patients). Preoperative MRIs were analyzed using the Visually AcceSAble Rembrandt Images and radiomics. The molecular glioblastoma status was determined on the basis of the presence of EGFR amplification and TERT promoter mutation. Molecular glioblastoma was present in 73.4% and 56.1% in the training and test sets, respectively. Models using clinical, Visually AcceSAble Rembrandt Images, and radiomic features were built to predict the molecular glioblastoma status in the training set; then they were validated in the test set.

Results: In the test set, a model using both Visually AcceSAble Rembrandt Images and radiomic features showed superior predictive performance (area under the curve = 0.854) than that with only clinical features or Visually AcceSAble Rembrandt Images (areas under the curve = 0.514 and 0.648, respectively; P < . 001, both). When both Visually AcceSAble Rembrandt Images and radiomics were added to clinical features, the predictive performance significantly increased (areas under the curve = 0.514 versus 0.863, P < .001).

Conclusions: MR imaging features integrated with machine learning classifiers may predict a subset of IDH wild-type lower-grade gliomas that carry molecular features of glioblastoma.
Full Text
http://www.ajnr.org/content/42/3/448.long
DOI
10.3174/ajnr.A6983
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
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
Kim, Hwiyoung(김휘영)
Park, Yae Won(박예원) ORCID logo https://orcid.org/0000-0001-8907-5401
Park, Chae Jung(박채정) ORCID logo https://orcid.org/0000-0002-5567-8658
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
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184130
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