Noninvasive prediction of IDH mutation status in gliomas using preoperative multiparametric MRI radiomics nomogram: A mutlicenter study

To establish and validate a radiomics nomogram for preoperative prediction of isocitrate dehydrogenase (IDH) mutation status of gliomas in a multicenter setting. 414 gliomas patients were collected (306 from local institution and 108 from TCGA). 851 radiomics features were extracted from contrast-en...

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Bibliographic Details
Published in:Magnetic resonance imaging 2023-12, Vol.104, p.72-79
Main Authors: Lu, Jun, Xu, Wenjuan, Chen, Xiaocao, Wang, Tan, Li, Hailiang
Format: Article
Language:English
Subjects:
Adult
Aged
Brain Neoplasms - diagnostic imaging
Brain Neoplasms - genetics
Female
Glioma
Glioma - diagnostic imaging
Glioma - genetics
Humans
IDH mutation
Isocitrate Dehydrogenase - genetics
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Male
Middle Aged
Multicenter study
Multiparametric Magnetic Resonance Imaging - methods
Mutation
Nomograms
Radiomics
ROC Curve
Young Adult
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Summary:To establish and validate a radiomics nomogram for preoperative prediction of isocitrate dehydrogenase (IDH) mutation status of gliomas in a multicenter setting. 414 gliomas patients were collected (306 from local institution and 108 from TCGA). 851 radiomics features were extracted from contrast-enhanced T1-weighted (CE-T1W) and fluid attenuated inversion recovery (FLAIR) sequence, respectively. The features were refined using least absolute shrinkage and selection operator (LASSO) regression combing 10-fold cross-validation. The optimal radiomics features with age and sex were processed by multivariate logistic regression analysis to construct a prediction model, which was developed in the training dataset and assessed in the test and validation dataset. Receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis were applied in the test and external validation datasets to evaluate the performance of the prediction model. Ten robust radiomics features were selected from the 1702 features (four CE-T1W features and six FLAIR features). A nomogram was plotted to represent the prediction model. The accuracy and AUC of the radiomics nomogram achieved 86.96% and 0.891(0.809–0.947) in the test dataset and 84.26% and 0.881(0.805–0.936) in the external validation dataset (all p 
ISSN:0730-725X
1873-5894
1873-5894
DOI:10.1016/j.mri.2023.09.001