Prediction of Tumor-Infiltrating CD8+T-Cell Expression in Glioblastoma Based on MRI Radiomics
10.3969/j.issn.1005-5185.2025.10.010
- VernacularTitle:基于MRI影像组学预测胶质母细胞瘤肿瘤浸润CD8+T细胞表达
- Author:
Caiqiang XUE
1
;
Xiaoai KE
;
Qing ZHOU
;
Ying WEI
;
Feng SHI
;
Bin ZHANG
;
Peng ZHANG
;
Hong LIU
;
Junlin ZHOU
Author Information
1. 青岛大学附属医院放射科,山东 青岛 266003;兰州大学第二医院放射科,甘肃 兰州 730030
- Publication Type:Journal Article
- Keywords:
Glioblastoma;
Magnetic resonance imaging;
Diffusion weighted imaging;
CD8-positive T-lymphocytes;
Radiomics;
Neoplasm invasiveness;
Forecasting
- From:
Chinese Journal of Medical Imaging
2025;33(10):1085-1091
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To evaluate the value of preoperative MRI-based radiomic models for assessing tumor-infiltrating CD8+T-cell expression in glioblastoma patients,and to identify the most stable and efficient radiomic feature region for predicting prognosis following immunotherapy.Materials and Methods This retrospective study included 150 patients with histopathologically confirmed glioblastoma from Lanzhou University Second Hospital(January 2018 to April 2022).Tumor-infiltrating CD8+T-cell expression was quantitatively assessed using immunohistochemical staining,with patients stratified into CD8-high and CD8-low expression groups based on overall survival.A total of 1 185 radiomic features were extracted from each patient's contrast-enhanced T1C and T2WI images,covering the original tumor region and sequentially expanded peritumoral regions(2.5 mm,5.0 mm,7.5 mm,10.0 mm,12.5 mm,15.0 mm morphological dilation of tumor core+peritumoral area).Feature selection was performed using variance threshold,minimum redundancy maximum relevance,and least absolute shrinkage and selection operator methods.XGBoost classifier was employed to construct clinical,radiomic,and clinical-radiomic multimodal combined prediction models.Diagnostic performance was evaluated using receiver operating characteristic curve analysis.Results The radiomic model based on tumor expansion of 7.5 mm(tumor+peritumoral region)demonstrated optimal predictive performance.The clinical-radiomic multimodal combined model showed superior predictive capability compared to clinical and radiomic models alone,achieving an area under the curve of 0.991 and accuracy of 99.0%in the training set,and area under the curve of 0.840 with accuracy of 80.0%in the validation set.Conclusion MRI radiomics provides a feasible approach for evaluating tumor-infiltrating CD8+T-cell expression in glioblastoma patients,offering potential for preoperative prognosis prediction.
