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• Clinical Article •
Application of quantitative magnetic resonance diffusion white matter analysis in the observation of white matter changes in low-grade glioma-associated epilepsy
GAO Ankang  GAO Eryuan  QI Jinbo  ZHAO Kai  ZHAO Gaoyang  CHEN Ting  ZHANG Huiting  YAN Xu  ZHAO Guohua  MA Xiaoyue  BAI Jie  ZHANG Yong  CHENG Jingliang 

GAO A K, GAO E Y, QI J B, et al. Application of quantitative magnetic resonance diffusion white matter analysis in the observation of white matter changes in low-grade glioma-associated epilepsy[J]. Chin J Magn Reson Imaging, 2023, 14(8): 10-18. DOI:10.12015/issn.1674-8034.2023.08.002.


[Abstract] Objective To observe the effect of low-grade glioma (LGG) tumor and peritumoral white matter changes in the occurrence of glioma-associated epilepsy (GAE) by magnetic resonance diffusion white matter quantification analysis.Materials and Methods The clinical and imaging information of patients with LGG confirmed by pathology who underwent diffusion spectrum imaging (DSI) in the First Affiliated Hospital of Zhengzhou University from December 2018 to December 2020 was retrospectively analyzed. A total of 102 patients with WHO Ⅱ low-grade gliomas were enrolled, including 37 patients with preoperative GAE and 65 patients without preoperative GAE. Diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator (MAP) metrics. ITK snap was used to draw tumor and peritumoral regions of interest (ROI) were based on b=0 diffusion images. FAE was used to perform histogram feature extraction, volume calculation of interest, and morphological feature extraction. After single parameter analysis and collinear analysis, logistic regression models were constructed based on each diffusion model and ROIs, and the DeLong test was used to compare the performance of models.Results There is a statistical difference in age between GAE groups (P=0.004). The incidence of GAE in patients with tumors located in the right hemisphere and trans hemisphere growth was lower than that in patients with tumors located in the left hemisphere, with a statistically significant difference (P=0.002). GAE predictive clinical-imaging model is constructed by age and hemispheric location of tumor, with AUC=0.779. The tumor and peritumoral volumes in the GAE group were significantly smaller than those in the non-GAE group (P＜0.05). There was no statistical difference in the morphological characteristics of the tumor area. The smaller the long and short diameters of the peritumoral area, the smaller the surface area, the more likely it is to be spherical, with higher incidence of GAE, and the difference is statistically significant (P＜0.05); the AUC value of constructing a GAE logistic regression model using morphological features of the peritumoral area can reach 0.730. Histogram features of quantitative parameters of diffusion models in tumor and peritumor areas with differences between GAE and non-GAE groups (P＜0.05), which including DTI_FA_Maximum, NODDI_ODI_90Percentile, MAP_NG_10Percentile. NODDI_ODI_90Percentile value of the peritumoral area in the GAE group was higher than that in the non-GAE group. The remaining features in tumor and peritumoral areas of GAE group were lower than the non-GAE group. Logistical models based on tumor area and peritumoral area showed no statistically significant difference in predictive performance of GAE, while the tumor area model had slightly higher performance than the peritumor area model. The combined model constructed by combining the features based on tumor area and peritumoral area have the highest performance, with a statistically significant difference compared to model based on the peritumor area (P=0.02) only. In the combined model, tumor features account for the majority, and the tumor DTI_MD_10Percentiles has the highest OR value, which positively correlated with the occurrence of GAE. The highest OR value among all models is the NODDI_ODI_Mean based on peritumoral feature, which is positively correlated with the occurrence of GAE. The MAP model has slightly higher performance than the DTI and NODDI models based on the individual diffusion models in the tumor area and peritumor area. There was no statistically significant difference in the predictive performance of GAE among clinical-imaging model, peritumoral morphological model, and combined models based on diffusion parameters.Conclusions Quantitative analysis of white matter is a promising way to predict the occurrence and mechanism of GAE. White matter damage in the tumor area, accompanied by increased dispersity or relatively intact white matter in the peritumoral area, increases the risk of GAE.
[Keywords] low grade glioma；magnetic resonance diffusion imaging；magnetic resonance imaging；brain white matter；glioma-associated epilepsy；histogram

GAO Ankang1   GAO Eryuan1   QI Jinbo1   ZHAO Kai1   ZHAO Gaoyang1   CHEN Ting1   ZHANG Huiting2   YAN Xu2   ZHAO Guohua1   MA Xiaoyue1   BAI Jie1   ZHANG Yong1   CHENG Jingliang1*  

1 Department of MR, the First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China

2 Department of MR Scientific Marketing, Siemens Healthineers, Shanghai 201318, China

Corresponding author: Cheng JL, E-mail: fccchengjl@zzu.edu.cn

Conflicts of interest   None.

ACKNOWLEDGMENTS Henan Science and Technology Research Program Joint Construction Project (No. LHGJ20220403).
Received  2023-03-17
Accepted  2023-07-27
DOI: 10.12015/issn.1674-8034.2023.08.002
GAO A K, GAO E Y, QI J B, et al. Application of quantitative magnetic resonance diffusion white matter analysis in the observation of white matter changes in low-grade glioma-associated epilepsy[J]. Chin J Magn Reson Imaging, 2023, 14(8): 10-18. DOI:10.12015/issn.1674-8034.2023.08.002.

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