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Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach
DC Field | Value | Language |
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dc.contributor.author | 강석구 | - |
dc.contributor.author | 김세훈 | - |
dc.contributor.author | 김의현 | - |
dc.contributor.author | 배소희 | - |
dc.contributor.author | 안성수 | - |
dc.contributor.author | 이승구 | - |
dc.contributor.author | 장종희 | - |
dc.contributor.author | 최윤성 | - |
dc.date.accessioned | 2018-10-11T08:55:31Z | - |
dc.date.available | 2018-10-11T08:55:31Z | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/163471 | - |
dc.description.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. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Springer International | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.title | Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Neurosurgery | - |
dc.contributor.googleauthor | Hie Bum Suh | - |
dc.contributor.googleauthor | Yoon Seong Choi | - |
dc.contributor.googleauthor | Sohi Bae | - |
dc.contributor.googleauthor | Sung Soo Ahn | - |
dc.contributor.googleauthor | Jong Hee Chang | - |
dc.contributor.googleauthor | Seok-Gu Kang | - |
dc.contributor.googleauthor | Eui Hyun Kim | - |
dc.contributor.googleauthor | Se Hoon Kim | - |
dc.contributor.googleauthor | Seung-Koo Lee | - |
dc.identifier.doi | 10.1007/s00330-018-5368-4 | - |
dc.contributor.localId | A00036 | - |
dc.contributor.localId | A00610 | - |
dc.contributor.localId | A00837 | - |
dc.contributor.localId | A04752 | - |
dc.contributor.localId | A02234 | - |
dc.contributor.localId | A02912 | - |
dc.contributor.localId | A03470 | - |
dc.contributor.localId | A04137 | - |
dc.relation.journalcode | J00851 | - |
dc.identifier.eissn | 1432-1084 | - |
dc.identifier.pmid | 29626238 | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s00330-018-5368-4 | - |
dc.subject.keyword | Glioblastoma | - |
dc.subject.keyword | Lymphoma | - |
dc.subject.keyword | Machine-learning | - |
dc.subject.keyword | Magnetic resonance imaging | - |
dc.subject.keyword | Radiomics | - |
dc.contributor.alternativeName | Kang, Seok Gu | - |
dc.contributor.alternativeName | Kim, Se Hoon | - |
dc.contributor.alternativeName | Kim, Eui Hyun | - |
dc.contributor.alternativeName | Bae, Sohi | - |
dc.contributor.alternativeName | Ahn, Sung Soo | - |
dc.contributor.alternativeName | Lee, Seung Koo | - |
dc.contributor.alternativeName | Chang, Jong Hee | - |
dc.contributor.alternativeName | Choi, Yoon Seong | - |
dc.contributor.affiliatedAuthor | Kang, Seok Gu | - |
dc.contributor.affiliatedAuthor | Kim, Se Hoon | - |
dc.contributor.affiliatedAuthor | Kim, Eui Hyun | - |
dc.contributor.affiliatedAuthor | Bae, Sohi | - |
dc.contributor.affiliatedAuthor | Ahn, Sung Soo | - |
dc.contributor.affiliatedAuthor | Lee, Seung Koo | - |
dc.contributor.affiliatedAuthor | Chang, Jong Hee | - |
dc.contributor.affiliatedAuthor | Choi, Yoon Seong | - |
dc.citation.volume | 28 | - |
dc.citation.number | 9 | - |
dc.citation.startPage | 3832 | - |
dc.citation.endPage | 3839 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, Vol.28(9) : 3832-3839, 2018 | - |
dc.identifier.rimsid | 60420 | - |
dc.type.rims | ART | - |
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