全肿瘤多序列磁共振结构分析预测胶质母细胞瘤端粒酶逆转录酶启动子突变状态

IF 2.1 4区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Magnetic resonance imaging Pub Date : 2025-02-19 DOI:10.1016/j.mri.2025.110360

Bin Zhang , Qing Zhou , Caiqiang Xue , Peng Zhang , Xiaoai Ke , Yige Wang , Yuting Zhang , Liangna Deng , Mengyuan Jing , Tao Han , Fengyu Zhou , Wenjie Dong , Junlin Zhou

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引用次数: 0

摘要

目的探讨术前多序列磁共振结构分析（MRTA）预测idh野生型胶质母细胞瘤（IDHwt GB） TERT启动子突变状态的可行性。方法回顾性分析我院2018年11月至2023年6月111例IDHwt GB患者的临床及影像学资料作为训练集，分析2023年7月至2023年11月23例IDHwt GB患者的临床及影像学资料作为验证集。我们使用分子测序结果将训练集分为TERT启动子突变组和野生型组。提取整个肿瘤体积的纹理特征，包括t2加权成像（T2WI）、t2 -流体衰减反演恢复、表观扩散系数（ADC）图、对比增强t1加权成像（CE-T1）。所有纹理特征均使用开源pyradiomics获得。特征选择完成后，利用逻辑回归建立预测模型，生成nomogram。最后，采用验证队列对模型进行验证。结果ce - t_1模型（AUC为0.704）的预测能力优于T2_Model （AUC为0.684）和ADC_Model （AUC为0.624）。MRI_Combined_Model （CE-T1、T2和ADC纹理特征）（AUC 0.780）的预测能力优于Clinical_Model （AUC 0.758）。结合CE-T1、T2、ADC纹理特征和临床特征的Combined_Model预测效果最佳（AUC 0.871），敏感性为82.60%，特异性为83.30%，准确率为80.18%。验证队列的AUC、敏感性、特异性和准确性分别为0.775%、86.70%、75.00%和69.57%。结论全肿瘤多序列MRTA可作为无创定量参数，辅助IDHwt GB患者TERT启动子突变状态的术前临床预测。

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Predicting telomerase reverse transcriptase promoter mutation status in glioblastoma by whole-tumor multi-sequence magnetic resonance texture analysis

Objective

This study aimed to determine the feasibility of preoperative multi-sequence magnetic resonance texture analysis (MRTA) for predicting TERT promoter mutation status in IDH-wildtype glioblastoma (IDHwt GB).

Methods

The clinical and imaging data of 111 patients with IDHwt GB at our hospital between November 2018 and June 2023 were retrospectively analyzed as the training set, and those of 23 patients with IDHwt GB between July 2023 and November 2023 were interpreted as the validation set. We used molecular sequencing results to classify the training set into TERT promoter mutation and wildtype groups. Textural features of the whole-tumor volume were extracted, including T2-weighted imaging (T2WI), T2-fluid-attenuated inversion recovery, apparent diffusion coefficient (ADC) map, and contrast-enhanced T1-weighted imaging (CE-T1). All textural features were obtained using open-source pyradiomics. After feature selection, logistic regression was used to build prediction models, and a nomogram was generated. Finally, the model was validated using validation cohort.

Results

The CE-T1_Model (AUC 0.704) had a better predictive ability than the T2_Model (AUC 0.684) and ADC_Model (AUC 0.624). The MRI_Combined_Model (CE-T1, T2, and ADC texture features) (AUC 0.780) had a better predictive ability than the Clinical_Model (AUC 0.758). The Combined_Model (CE-T1, T2, ADC texture features, and clinical features) had the best predictive performance (AUC 0.871), with a sensitivity, specificity, and accuracy of 82.60 %, 83.30 %, and 80.18 %, respectively. The AUC, sensitivity, specificity, and accuracy in the validation cohort were 0.775, 86.70 %, 75.00 %, and 69.57 %, respectively.

Conclusions

Whole-tumor multi-sequence MRTA can be used as non-invasive quantitative parameters to assist in the preoperative clinical prediction of TERT promoter mutation status in IDHwt GB.

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来源期刊

Magnetic resonance imaging 医学-核医学

CiteScore

4.70

自引率

4.00%

发文量

194

审稿时长

83 days

期刊介绍： Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.