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• Clinical Article •
Predicting IDH1 gene mutation of gliomas by combining clinical and imaging features with multiple sequence radiomics
HE Jinlong  GAO Yang  WU Qiong  LI Bo  WANG Peng 

HE J L, GAO Y, WU Q, et al. Predicting IDH1 gene mutation of gliomas by combining clinical and imaging features with multiple sequence radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(8): 27-33, 134. DOI:10.12015/issn.1674-8034.2023.08.004.


[Abstract] Objective To explore the value of multi-sequence radiomics features and clinical related parameters in predicting isocitrate dehydrogenase 1 (IDH1) gene mutations in gliomas.Materials andMethods A total of 81 patients with gliomas confirmed by histopathology and containing IDH1 gene mutation status information were analyzed retrospectively. Five types of images of T2WI, T1WI, diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast enhancement MRI (CE-MRI) were applied for radiomics feature extraction. Each sequence can extract 107 radiomics features. The above features were subjected to single factor rank sum test, correlation analysis, and least absolute shrinkage selection operator (LASSO) dimensionality reduction screening. Multivariate logistic regression was used to establish various sequence models and multiple sequence fusion models for the remaining features, including T2WI model, T1WI model, DWI model, ADC model, CE-MRI model, and multiple sequence radiomics model. Finally, a combined model is established by combining the Radscores output from the multi sequence radiomics model with the clinical multivariate model. The above models used receiver operating characteristic (ROC) curves to analyze the predictive performance of each model, and compared the differences in area under the curve (AUC) using DeLong non parametric tests. In addition, decision curve analysis (DCA) was used to evaluate the clinical benefits of multiple sequence radiomics models and combined models in identifying IDH1 gene mutation status.Results The combined model showed the best performance in predicting IDH1 gene mutations in gliomas (AUC: 0.928). The AUC values of multiple sequence radiomics models were higher than those of T2WI, DWI, and ADC models (0.865 vs. 0.752, 0.656, 0.631, P＜0.05, respectively); The AUC value of the combined model was higher than that of T2WI, T1WI, T1 enhanced, and multi sequence radiomics models (0.928 vs. 0.752, 0.827, 0.829, 0.865, P＜0.05, respectively); However, there was no statistically significant difference in AUC values between the combined model and the clinical model (0.928 and 0.880, respectively, P＞0.05). The decision curve analysis showed that the combined model had higher clinical benefits in identifying IDH1 gene mutations with sequence radiomics models.Conclusions The combination of multi-sequence radiomics features, clinical and MRI imaging features has important value in preoperative differentiation of IDH1 gene mutations in gliomas.
[Keywords] glioma of the brain；isocitrate dehydrogenase 1 gene mutation；radiomics；model prediction；magnetic resonance imaging

HE Jinlong   GAO Yang*   WU Qiong   LI Bo   WANG Peng  

Department of Imaging Diagnosis, the Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010030, China

Corresponding author: Gao Y, E-mail: 1390903990@qq.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Project of Science and Technology Department of Inner Mongolia (No. 2019GG047); Surface Project of Inner Mongolia Medical University (No. YKD2022MS039).
Received  2022-05-09
Accepted  2023-07-21
DOI: 10.12015/issn.1674-8034.2023.08.004
HE J L, GAO Y, WU Q, et al. Predicting IDH1 gene mutation of gliomas by combining clinical and imaging features with multiple sequence radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(8): 27-33, 134. DOI:10.12015/issn.1674-8034.2023.08.004.

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