Park, Ji Eun
Ham, Sungwon
Kim, Ho Sung http://orcid.org/0000-0002-9477-7421
Park, Seo Young
Yun, Jihye
Lee, Hyunna
Choi, Seung Hong
Kim, Namkug
Funding for this research was provided by:
National Research Council of Science and Technology (NRF-2020R1A2B5B01001707, NRF-2020R1A2C4001748)
Article History
Received: 7 July 2020
Revised: 25 August 2020
Accepted: 13 October 2020
First Online: 31 October 2020
Compliance with ethical standards
:
: The scientific guarantor of this publication is Namkug Kim.
: The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.
: One of the authors has significant statistical expertise (Seo Young Park, 8 years of experienced statistician).
: Written informed consent was waived by the Institutional Review Board.
: Institutional Review Board approval was obtained.
: The external 33 post-treatment glioblastoma patients were in part of a previously reported study (Kim JY et al <i>Neuro-Oncology</i>, Volume 21, Issue 3, March 2019, Pages 404–414, ExternalRef removed). This prior article dealt with development of multiparametric MRI radiomics model using manual segmentation whereas in this manuscript we report reproducibility and accuracy from DLAS-obtained radiomics features and focused on CE-T1w imaging. The method is totally different that the previous study used feature selection in order to create the model and only 12 features were used. Meanwhile what we did here was using entire 1618 extracted features and used random forest classifier to calculate diagnostic performance, with the aim to measure the effect of segmentation to subsequent feature extraction. Also, we created the DLAS model in this study while previous report did manual segmentation.
: • retrospective• cross-sectional study• multicentre study