Abstract：
:We aimed to develop and validate a multiparametric MR radiomics model using conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in patients with newly diagnosed glioblastoma. A total of 216 patients with newly diagnosed glioblastoma were enrolled from two tertiary medical centers and divided into training (n = 158) and external validation sets (n = 58). Radiomic features were extracted from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. After radiomic feature selection using LASSO regression, an individualized radiomic score was calculated. A multiparametric MR prognostic model was built using the radiomic score and clinical predictors. The results showed that the multiparametric MR prognostic model (radiomics score + clinical predictors) exhibited good discrimination (C-index, 0.74) and performed better than a conventional MR radiomics model (C-index, 0.65, P < 0.0001) or clinical predictors (C-index, 0.66; P < 0.0001). The multiparametric MR prognostic model also showed robustness in external validation (C-index, 0.70). Our results indicate that the incorporation of diffusion- and perfusion-weighted MR imaging into an MR radiomics model to improve prognostication in glioblastoma patients improved its performance over that achievable using clinical predictors alone.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Park JE,Kim HS,Jo Y,Yoo RE,Choi SH,Nam SJ,Kim JHdoi
10.1038/s41598-020-61178-wsubject
Has Abstractpub_date
2020-03-06 00:00:00pages
4250issue
1issn
2045-2322pii
10.1038/s41598-020-61178-wjournal_volume
10pub_type
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pub_type： 杂志文章
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pub_type： 临床试验,杂志文章
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