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- Title
Glioma Type Prediction with Dynamic Contrast-Enhanced MR Imaging and Diffusion Kurtosis Imaging—A Standardized Multicenter Study.
- Authors
Zerweck, Leonie; Hauser, Till-Karsten; Klose, Uwe; Han, Tong; Nägele, Thomas; Shen, Mi; Gohla, Georg; Estler, Arne; Xie, Chuanmiao; Hu, Hongjie; Yang, Songlin; Cao, Zhijian; Erb, Gunter; Ernemann, Ulrike; Richter, Vivien
- Abstract
Simple Summary: The identification of gliomas and the differentiation between different types is essential to evaluate patients' prognosis and guide optimal clinical management. The ideal multiparametric magnetic resonance imaging (MRI) protocol for the assessment of gliomas is a current topic of research. This study aimed to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating molecular subtypes of adult-type diffuse gliomas. The results showed that a combined evaluation of DCE-MRI and DKI parameters reveals the best prediction of high-grade vs. low-grade gliomas, IDH1/2 wildtype vs. mutated gliomas, and astrocytomas/glioblastomas vs. oligodendrogliomas. The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2–4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900–1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702–0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700–0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification.
- Subjects
BRAIN tumor diagnosis; REFERENCE values; PREDICTIVE tests; GLIOMAS; DIAGNOSTIC imaging; RECEIVER operating characteristic curves; PREDICTION models; RESEARCH funding; LOGISTIC regression analysis; MAGNETIC resonance imaging; QUANTITATIVE research; DESCRIPTIVE statistics; PERFUSION imaging; RESEARCH; WHITE matter (Nerve tissue); PERFUSION; CONTRAST media; SENSITIVITY &; specificity (Statistics); BRAIN tumors
- Publication
Cancers, 2024, Vol 16, Issue 15, p2644
- ISSN
2072-6694
- Publication type
Article
- DOI
10.3390/cancers16152644