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Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach

Authors
 Hie Bum Suh  ;  Yoon Seong Choi  ;  Sohi Bae  ;  Sung Soo Ahn  ;  Jong Hee Chang  ;  Seok-Gu Kang  ;  Eui Hyun Kim  ;  Se Hoon Kim  ;  Seung-Koo Lee 
Citation
 European Radiology, Vol.28(9) : 3832-3839, 2018 
Journal Title
 European Radiology 
ISSN
 0938-7994 
Issue Date
2018
Keywords
Glioblastoma ; Lymphoma ; Machine-learning ; Magnetic resonance imaging ; Radiomics
Abstract
OBJECTIVES: To evaluate the diagnostic performance of magnetic resonance (MR) radiomics-based machine-learning algorithms in differentiating primary central nervous system lymphoma (PCNSL) from non-necrotic atypical glioblastoma (GBM). METHODS: Seventy-seven patients (54 individuals with PCNSL and 23 with non-necrotic atypical GBM), diagnosed from January 2009 to April 2017, were enrolled in this retrospective study. A total of 6,366 radiomics features, including shape, volume, first-order, texture, and wavelet-transformed features, were extracted from multi-parametric (post-contrast T1- and T2-weighted, and fluid attenuation inversion recovery images) and multiregional (enhanced and non-enhanced) tumour volumes. These features were subjected to recursive feature elimination and random forest (RF) analysis with nested cross-validation. The diagnostic abilities of a radiomics machine-learning classifier, apparent diffusion coefficient (ADC), and three readers, who independently classified the tumours based on conventional MR sequences, were evaluated using receiver operating characteristic (ROC) analysis. Areas under the ROC curves (AUC) of the radiomics classifier, ADC value, and the radiologists were compared. RESULTS: The mean AUC of the radiomics classifier was 0.921 (95 % CI 0.825-0.990). The AUCs of the three readers and ADC were 0.707 (95 % CI 0.622-0.793), 0.759 (95 %CI 0.656-0.861), 0.695 (95 % CI 0.590-0.800) and 0.684 (95 % CI0.560-0.809), respectively. The AUC of the radiomics-based classifier was significantly higher than those of the three readers and ADC (p< 0.001 for all). CONCLUSIONS: Large-scale radiomics with a machine-learning algorithm can be useful for differentiating PCNSL from atypical GBM, and yields a better diagnostic performance than human radiologists and ADC values.
DOI
10.1007/s00330-018-5368-4
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/22282913/163471
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