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• Clinical Article •
Prediction based on CE-T1WI omics and pathological parameter models research on postoperative recurrence of glioma
JIN Yixuan  LÜ Ruirui  YANG Zhihua  SUN Meng  NIU Fang  LÜ Hongjie  MA Rong  WANG Xiaodong 

Cite this article as: JIN Y X, LÜ R R, YANG Z H, et al. Prediction based on CE-T1WI omics and pathological parameter models research on postoperative recurrence of glioma[J]. Chin J Magn Reson Imaging, 2024, 15(10): 103-108. DOI:10.12015/issn.1674-8034.2024.10.018.


[Abstract] Objective To explore the application value of using a column chart based on preoperative contrast enhancement T1WI (CE-T1WI) omics combined with pathological parameters to predict postoperative recurrence in patients with gliomas.Materials and Methods A retrospective analysis was conducted on 115 patients diagnosed with glioma after surgery at the General Hospital of Ningxia Medical University from April 2020 to April 2023. They were randomly divided into a training set (n=81) and a validation set (n=34) at a ratio of 7∶3. Draw the volume of interest (VOI) on preoperative enhanced T1WI (CE-T1WI) and extract imaging omics features. U test and least absolute shrinkage and selection operator (LASSO) algorithm were used to screen imaging omics features. The final selected features were included in imaging omics labels and an imaging omics model was established. Calculate the radiomics score (Radscore) based on the corresponding coefficients of the selected omics features. Screening pathological predictive factors that are correlated with recurrence through logistic regression and establishing a pathological parameter model. The combination of the two forms a joint model, and a column chart is drawn to visualize the joint model. Evaluate the predictive performance of each model using the area under curve (AUC) of the subject's working characteristic curve. Using the DeLong test to compare the differences in AUC values between different models, and observe the clinical value of each model using decision curve analysis (DCA).Results Based on preoperative CE-T1WI image delineation, a total of 200 imaging omics features were extracted from VOI, and 6 omics features related to recurrence were selected. Logistic regression analysis was used to include isocitrate dehydrogenase 1 (IDH-1) genotype (OR=2.070, P=0.041) and Ki-67 expression level (OR=1.065, P<0.001) as pathological parameters associated with glioma recurrence. Compared to individual pathological parameter models and radiomics models, the combined model showed the best predictive performance (AUC: 0.875 vs. 0.835, 0.769 in the training group, Z=-1.585, -2.458, P=0.013, 0.014). DCA analysis showed that when the probability of risk threshold was greater than 0.32, the clinical benefit level of using the combined model was higher than the other two models.Conclusions The combined model based on preoperative CE-T1WI imaging omics and pathological parameters has good clinical application value in predicting glioma recurrence, providing important predictive information for treatment decision-making and prognosis of glioma patients.
[Keywords] glioma；recurrence；imaging omics；magnetic resonance imaging；column chart；prediction model

JIN Yixuan1   LÜ Ruirui2   YANG Zhihua3   SUN Meng1   NIU Fang1   LÜ Hongjie1   MA Rong2   WANG Xiaodong1*  

1 Ningxia Medical University School of Clinical Medicine, Yinchuan 750004, China

2 Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750004, China

3 Department of Radiotherapy, Cancer Hospital of General Hospital of Ningxia Medical University, Yinchuan 750004, China

Corresponding author: WANG X D, E-mail: xdw80@yeah.net

Conflicts of interest   None.

Received  2024-06-11
Accepted  2024-10-10
DOI: 10.12015/issn.1674-8034.2024.10.018
Cite this article as: JIN Y X, LÜ R R, YANG Z H, et al. Prediction based on CE-T1WI omics and pathological parameter models research on postoperative recurrence of glioma[J]. Chin J Magn Reson Imaging, 2024, 15(10): 103-108. DOI:10.12015/issn.1674-8034.2024.10.018.

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