Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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

Authors
Suh, Hie BumChoi, Yoon SeongBae, SohiAhn, Sung SooChang, Jong HeeKang, Seok-GuKim, Eui HyunKim, Se HoonLee, Seung-Koo
Issue Date
Sep-2018
Publisher
SPRINGER
Keywords
Lymphoma; Glioblastoma; Machine-learning; Magnetic resonance imaging; Radiomics
Citation
EUROPEAN RADIOLOGY, v.28, no.9, pp.3832 - 3839
Indexed
SCIE
SCOPUS
Journal Title
EUROPEAN RADIOLOGY
Volume
28
Number
9
Start Page
3832
End Page
3839
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/190959
DOI
10.1007/s00330-018-5368-4
ISSN
0938-7994
Abstract
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).,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.,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).,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.,aEuro cent Machine-learning algorithm radiomics can help to differentiate primary central PCNSL from GBM.,aEuro cent This approach yields a higher diagnostic accuracy than visual analysis by radiologists.,aEuro cent Radiomics can strengthen radiologists' diagnostic decisions whenever conventional MRI sequences are available.
Files in This Item
Go to Link
Appears in
Collections
서울 의과대학 > 서울 영상의학교실 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher Bae, Sohi photo

Bae, Sohi
COLLEGE OF MEDICINE (DEPARTMENT OF RADIOLOGY)
Read more

Altmetrics

Total Views & Downloads

BROWSE