Content area

Abstract

Purpose

An accurate assessment of the World Health Organization grade is vital for patients with pediatric gliomas to direct treatment planning. We aim to evaluate the diagnostic performance of whole-tumor histogram analysis of diffusion-weighted imaging (DWI) and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI) for differentiating pediatric high-grade gliomas from pediatric low-grade gliomas.

Methods

Sixty-eight pediatric patients (mean age, 10.47 ± 4.37 years; 42 boys) with histologically confirmed gliomas underwent preoperative MR examination. The conventional MRI features and whole-tumor histogram features extracted from apparent diffusion coefficient (ADC) and cerebral blood volume (CBV) maps were analyzed, respectively. Receiver operating characteristic curves and the binary logistic regression analysis were performed to determine the diagnostic performance of parameters.

Results

For conventional MRI features, location, hemorrhage and tumor margin showed significant difference between pediatric high- and low-grade gliomas (all, P < .05). For advanced MRI parameters, ten histogram features of ADC and CBV showed significant differences between pediatric high- and low-grade gliomas (all, P < .05). The diagnostic performance of the combination of DSC-PWI and DWI (AUC = 0.976, sensitivity = 100%, NPV = 100%) is superior to conventional MRI or DWI model, respectively (AUCcMRI = 0.700, AUCDWI = 0.830; both, P < .05).

Conclusion

The whole-tumor histogram analysis of DWI and DSC-PWI is a promising method for grading pediatric gliomas.

Details

Title
Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading
Author
Su, Yan 1 ; Kang, Jie 1 ; Lin, Xiang 1 ; She, Dejun 1 ; Guo, Wei 1 ; Xing, Zhen 1 ; Yang, Xiefeng 1 ; Cao, Dairong 2   VIAFID ORCID Logo 

1  The First Affiliated Hospital of Fujian Medical University, Department of Radiology, Fuzhou, China (GRID:grid.412683.a) (ISNI:0000 0004 1758 0400) 
2  The First Affiliated Hospital of Fujian Medical University, Department of Radiology, Fuzhou, China (GRID:grid.412683.a) (ISNI:0000 0004 1758 0400); the First Affiliated Hospital, Fujian Medical University, Department of Radiology, Fujian Key Laboratory of Precision Medicine for Cancer, Fuzhou, China (GRID:grid.412683.a) (ISNI:0000 0004 1758 0400); National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Department of Radiology, Fuzhou, China (GRID:grid.256112.3) (ISNI:0000 0004 1797 9307) 
Pages
1063-1071
Publication year
2023
Publication date
Jun 2023
Publisher
Springer Nature B.V.
ISSN
00283940
e-ISSN
14321920
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1007/s00234-023-03145-6
ProQuest document ID
2811379892
Copyright
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.