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Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction

Authors
 Sohi Bae  ;  Yoon Seong Choi  ;  Sung Soo Ahn  ;  Jong Hee Chang  ;  Seok-Gu Kang  ;  Eui Hyun Kim  ;  Se Hoon Kim  ;  Seung-Koo Lee 
Citation
 Radiology, Vol.289(3) : 797-806, 2018 
Journal Title
 Radiology 
ISSN
 0033-8419 
Issue Date
2018
Abstract
Purpose To investigate whether radiomic features at MRI improve survival prediction in patients with glioblastoma multiforme (GBM) when they are integrated with clinical and genetic profiles. Materials and Methods Data in patients with a diagnosis of GBM between December 2009 and January 2017 (217 patients) were retrospectively reviewed up to May 2017 and allocated to training and test sets (3:1 ratio). Radiomic features (n = 796) were extracted from multiparametric MRI. A random survival forest (RSF) model was trained with the radiomic features along with clinical and genetic profiles (O-6-methylguanine-DNA-methyltransferase promoter methylation and isocitrate dehydrogenase 1 mutation statuses) to predict overall survival (OS) and progression-free survival (PFS). The RSF models were validated on the test set. The incremental values of radiomic features were evaluated by using the integrated area under the receiver operating characteristic curve (iAUC). Results The 217 patients had a mean age of 57.9 years, and there were 87 female patients (age range, 22-81 years) and 130 male patients (age range, 17-85 years). The median OS and PFS of patients were 352 days (range, 20-1809 days) and 264 days (range, 21-1809 days), respectively. The RSF radiomics models were successfully validated on the test set (iAUC, 0.652 [95% confidence interval {CI}, 0.524, 0.769] and 0.590 [95% CI: 0.502, 0.689] for OS and PFS, respectively). The addition of a radiomics model to clinical and genetic profiles improved survival prediction when compared with models containing clinical and genetic profiles alone (P = .04 and .03 for OS and PFS, respectively). Conclusion Radiomic MRI phenotyping can improve survival prediction when integrated with clinical and genetic profiles and thus has potential as a practical imaging biomarker. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Jain and Lui in this issue.
Full Text
https://pubs.rsna.org/doi/10.1148/radiol.2018180200
DOI
10.1148/radiol.2018180200
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
강석구(Kang, Seok Gu) ORCID logo https://orcid.org/0000-0001-5676-2037
김세훈(Kim, Se Hoon) ORCID logo https://orcid.org/0000-0001-7516-7372
김의현(Kim, Eui Hyun) ORCID logo https://orcid.org/0000-0002-2523-7122
배소희(Bae, Sohi)
안성수(Ahn, Sung Soo) ORCID logo https://orcid.org/0000-0002-0503-5558
이승구(Lee, Seung Koo) ORCID logo https://orcid.org/0000-0001-5646-4072
장종희(Chang, Jong Hee)
최윤성(Choi, Yoon Seong)
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URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/166769
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