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Clinical Articles
Prediction of IDH mutations in glioma based on MRI multiparametric image fusion and DenseNet network
HU Zhenyuan  WEI Wei  HU Wenzhong  MA Menghang  LI Yan  WU Xusha  YIN Hong  XI Yibin 

Cite this article as: HU Z Y, WEI W, HU W Z, et al. Prediction of IDH mutations in glioma based on MRI multiparametric image fusion and DenseNet network[J]. Chin J Magn Reson Imaging, 2023, 14(7): 10-17. DOI:10.12015/issn.1674-8034.2023.07.003.

[Abstract] Objective Developing a high-accuracy prediction model based on artificial intelligence deep learning DenseNet network and multimodal fusion technology to predict the preoperative isocitrate dehydrogenase (IDH) gene mutation status in glioma patients.Materials and Methods Retrospective analysis of the preoperative multisequence MRI scan images of 256 (155 IDH wild type and 101 IDH mutant type) patients consecutively admitted to xijing hospital, air force military medical university, from January 2012 to September 2016, and the region of interest was outlined on T1-weighted imaging(T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI sequences; deep learning convolutional neural networks were used to extract and fuse the MRI multimodal features. The model performance differences between the multimodal fusion model and two simple stitching methods of multimodal features were quantitatively compared.Results The multimodal fusion had superior prediction performance than other single-modal simple splicing, achieving good discriminative performance with the training and testing set receiver operating characteristic curve area under the curve of 0.903 [95% confidence interval (CI), 0.845-0.961] and 0.904 (95% CI, 0.842-0.966), respectively; accuracy of 91.3% and 88.7%, respectively. The sensitivity reached 86.4% and 90.5% respectively; the specificity reached 94.5% and 87.5% respectively, and the model consistency was verified using the calibration curve, and the model calibration graph is close to the diagonal line, reflecting that the model has a good prediction effect. The DeLong test results showed a statistical difference (P<0.05) in the model performance between the two methods of multimodal fusion and ablation, with the former being superior to the latter.Conclusions MRI multimodal fusion model based on deep learning DenseNet network can achieve non-invasive and low-cost prediction of IDH gene status of glioma before surgery by integrating multimodal MRI image information of tumor.
[Keywords] glioma;deep learning;intelligent medicine;magnetic resonance imaging;multimodal fusion;IDH

HU Zhenyuan1   WEI Wei1   HU Wenzhong2   MA Menghang1   LI Yan3   WU Xusha3   YIN Hong2, 3   XI Yibin3*  

1 School of Electronic Information, Xi'an Polytechnic University, Xi'an 710600, China

2 Department of Radiology, Xijing Hospital of the Fourth Military Medical University, Xi'an 710032, China

3 Medical Imaging Center, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an 710004, China

Corresponding author: Xi YB, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Shanxi Provincial Natural Science Basic Research Program (No. 2023-JC-YB-682, 2023-JC-ZD-58); Xi'an Science and Technology Plan for Scientific and Technical Staff of Universities and Institutes to Serve Enterprises (No. 22GXFW0036).
Received  2022-12-30
Accepted  2023-06-26
DOI: 10.12015/issn.1674-8034.2023.07.003
Cite this article as: HU Z Y, WEI W, HU W Z, et al. Prediction of IDH mutations in glioma based on MRI multiparametric image fusion and DenseNet network[J]. Chin J Magn Reson Imaging, 2023, 14(7): 10-17. DOI:10.12015/issn.1674-8034.2023.07.003.

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