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The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas
Joint Authors
Liu, Xuejun
Niu, Lei
Feng, Wei-hua
Duan, Chong-feng
Liu, Ying-chao
Liu, Ji-hua
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-24
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Background.
The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment.
Purpose.
To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma.
Materials and Methods.
A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV).
Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K.
(Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses.
Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation.
ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model.
Results.
The radiomic model showed good discrimination in both the primary dataset (AUC=0.87, 95% CI: 0.754 to 0.855, ACC=0.798, sensitivity=85.5%, specificity=75.4%, positive predictive value=0.734, and negative predictive value=0.867) and the validation dataset (AUC=0.86, 95% CI: 0.690 to 0.913, ACC=0.789, sensitivity=91.3%, specificity=69.0%, positive predictive value=0.700, and negative predictive value=0.909).
Conclusion.
The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.
American Psychological Association (APA)
Niu, Lei& Feng, Wei-hua& Duan, Chong-feng& Liu, Ying-chao& Liu, Ji-hua& Liu, Xuejun. 2020. The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas. BioMed Research International،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1134049
Modern Language Association (MLA)
Niu, Lei…[et al.]. The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas. BioMed Research International No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1134049
American Medical Association (AMA)
Niu, Lei& Feng, Wei-hua& Duan, Chong-feng& Liu, Ying-chao& Liu, Ji-hua& Liu, Xuejun. The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1134049
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1134049