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• Clinical Article •
A preliminary study on predicting glioblastoma recurrence and postoperative survival time through MRI imaging radiomics
ZHAI Xiaoyang  REN Jinfa  CHENG Sijia  MAO Ke  DONG Yaning  HAN Dongming 

Cite this article as: ZHAI X Y, REN J F, CHENG S J, et al. A preliminary study on predicting glioblastoma recurrence and postoperative survival time through MRI imaging radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(12): 26-32. DOI:10.12015/issn.1674-8034.2023.12.005.


[Abstract] Objective To develop an evaluation model for predicting early postoperative recurrence and evaluating the prognosis of glioma patients using preoperative MRI radiomics and clinical features.Materials and Methods The MRI images and clinical data of 120 glioma patients were analyzed retrospectively to extract the imaging omics characteristics of the peritumoral edema areas and intratumoral enhancement areas. To compare the variables between the recurrence and non-recurrence groups, we utilized either Chi-square tests or Fisher's exact tests. Furthermore, differences between continuous variables were examined through t-tests or U tests. For dimensionality reduction of the features, we employed t-tests, Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression. Three distinct predictive models were established, including intratumoral, intratumoral plus peritumoral edema, and fusion models. Nomograms were employed to display the predictions of the 3-year survival period, while Kaplan-Meier (KM) plots were utilized to illustrate the survival outcomes across different groups.Results Statistically significant differences were observed in the isocitrate dehydrogenase (IDH) mutation status and Rad-score between the recurrence and non-recurrence groups, with P-values of 0.04 and＜0.001, respectively. The final analysis included fifteen imaging radiomics features. The three models in the training set displayed area under the curve (AUC) values of 0.905, 0.925, and 0.923, while in the test set, the corresponding AUC values were 0.859, 0.866, and 0.897. The fusion model outperformed the others. KM analysis demonstrated no significant differences in survival time among patient groups in both the training and test sets.Conclusions MRI-based imaging radiomics demonstrates promising predictive capability for postoperative recurrence in glioblastoma patients, while also offering an initial assessment of postoperative survival time.
[Keywords] glioma；radiomics；machine learning；survival time；magnetic resonance imaging

ZHAI Xiaoyang   REN Jinfa   CHENG Sijia   MAO Ke   DONG Yaning   HAN Dongming*  

Department of MRI, the First Affiliated Hospital of Xinxiang University, Xinxiang 453100, China

Corresponding author: HAN D M, E-mail: 625492590@qq.com

Conflicts of interest   None.

Received  2023-06-29
Accepted  2023-11-03
DOI: 10.12015/issn.1674-8034.2023.12.005
Cite this article as: ZHAI X Y, REN J F, CHENG S J, et al. A preliminary study on predicting glioblastoma recurrence and postoperative survival time through MRI imaging radiomics[J]. Chin J Magn Reson Imaging, 2023, 14(12): 26-32. DOI:10.12015/issn.1674-8034.2023.12.005.

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