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• Clinical Article •
Prediction of habitat subregions of the glioblastoma microenvironment based on multimodal MRI radiomics for MGMT promoter methylation expression
JIAO Kaijian  YANG Bo  CHEN Wen  FANG Yuhui  CHEN Yalin  WU Lei 

Cite this article as: JIAO K J, YANG B, CHEN W, et al. Prediction of habitat subregions of the glioblastoma microenvironment based on multimodal MRI radiomics for MGMT promoter methylation expression[J]. Chin J Magn Reson Imaging, 2023, 14(11): 25-30, 76. DOI:10.12015/issn.1674-8034.2023.11.005.


[Abstract] Objective To explore the efficacy of multimodal imaging radiomics models of different tumor microenvironment subregions in predicting the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in glioblastoma before surgery.Materials and Methods A retrospective analysis was conducted on preoperative MRI images, clinical, and genetic information of 600 glioblastoma patients from Taihe Hospital, Hubei University of Medicine, University of Pennsylvania, and University of California, San Francisco. The preprocessed images were automatically segmented to obtain three subregions of the tumor microenvironment. From the preoperative MRI images [contrast enhanced T1-weighted imaging (T1WI-CE), T2 fluid attenuation inversion recovery (T2-FLAIR) sequence, and diffusion tensor imaging (DTI) fractional anisotropy (FA) maps], 2 153 radiomics features were extracted from three habitat subregions, including enhanced region, necrotic region and edema region. Feature selection was performed using correlation analysis, minimum redundancy maximum relevance (MRMR), and Boruta algorithm, and the XGBoost algorithm was used to build classification model. The diagnostic performance of the models was evaluated using receiver operating characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, specificity, and DeLong test for model comparison.Results There were no statistically significant differences in the intergroup comparisons of clinical features between the two subtypes in the training and testing sets (P＞0.05). The multimodal imaging radiomics model for the enhanced region had AUCs of 0.842 and 0.935 in the training and validation sets, respectively. Ten features from the multimodal habitat subregions were obtained after feature selection. The AUC of the imaging omics model in the multimodal habitat subregion was 0.874 and 0.899 on the training and test sets, respectively.Conclusions The preoperative MRI radiomics models can predict the MGMT promoter methylation status in glioblastoma patients, and the multimodal combination models showed more robust diagnostic performance. The study of tumor microenvironment subregions provides important clinical utility for accurate molecular subtyping, decision-making for temozolomide (TMZ) use, and survival prediction in glioblastoma patients.
[Keywords] glioblastoma；radiomics；magnetic resonance imaging；O6-methylguanine-DNA methyltransferase promoter；habitat imaging

JIAO Kaijian1, 2   YANG Bo1, 2   CHEN Wen1, 2   FANG Yuhui1   CHEN Yalin1   WU Lei2*  

1 School of Biomedical Engineering Hubei University of Medicine, Shiyan 442000, China

2 Institute of Medical Imaging, Medical Imaging Center, Taihe Hospital, Hubei University of Medicine, Shiyan 442000, China

Corresponding author: WU L, E-mail: cookiebag@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS Nature Science Foundation of Hubei Province (No. 2022CFB853); Wu Jieping Medical Foundation Special Fund for Clinical Research (No. 320.6750.2020-08-6).
Received  2023-08-02
Accepted  2023-11-07
DOI: 10.12015/issn.1674-8034.2023.11.005
Cite this article as: JIAO K J, YANG B, CHEN W, et al. Prediction of habitat subregions of the glioblastoma microenvironment based on multimodal MRI radiomics for MGMT promoter methylation expression[J]. Chin J Magn Reson Imaging, 2023, 14(11): 25-30, 76. DOI:10.12015/issn.1674-8034.2023.11.005.

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