Zhang Hongbo, Zhang Hanwen, Zhang Yuze, Zhou Beibei, Wu Lei, Lei Yi, Huang Biao
The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
J Magn Reson Imaging. 2023 Nov;58(5):1441-1451. doi: 10.1002/jmri.28671. Epub 2023 Mar 10.
Studies have shown that magnetic resonance imaging (MRI)-based deep learning radiomics (DLR) has the potential to assess glioma grade; however, its role in predicting telomerase reverse transcriptase (TERT) promoter mutation status in patients with glioblastoma (GBM) remains unclear.
To evaluate the value of deep learning (DL) in multiparametric MRI-based radiomics in identifying TERT promoter mutations in patients with GBM preoperatively.
Retrospective.
A total of 274 patients with isocitrate dehydrogenase-wildtype GBM were included in the study. The training and external validation cohorts included 156 (54.3 ± 12.7 years; 96 males) and 118 (54 .2 ± 13.4 years; 73 males) patients, respectively.
FIELD STRENGTH/SEQUENCE: Axial contrast-enhanced T1-weighted spin-echo inversion recovery sequence (T1CE), T1-weighted spin-echo inversion recovery sequence (T1WI), and T2-weighted spin-echo inversion recovery sequence (T2WI) on 1.5-T and 3.0-T scanners were used in this study.
Overall tumor area regions (the tumor core and edema) were segmented, and the radiomics and DL features were extracted from preprocessed multiparameter preoperative brain MRI images-T1WI, T1CE, and T2WI. A model based on the DLR signature, clinical signature, and clinical DLR (CDLR) nomogram was developed and validated to identify TERT promoter mutation status.
The Mann-Whitney U test, Pearson test, least absolute shrinkage and selection operator, and logistic regression analysis were applied for feature selection and construction of radiomics and DL signatures. Results were considered statistically significant at P-value <0.05.
The DLR signature showed the best discriminative power for predicting TERT promoter mutations, yielding an AUC of 0.990 and 0.890 in the training and external validation cohorts, respectively. Furthermore, the DLR signature outperformed CDLR nomogram (P = 0.670) and significantly outperformed clinical models in the validation cohort.
The multiparameter MRI-based DLR signature exhibited a promising performance for the assessment of TERT promoter mutations in patients with GBM, which could provide information for individualized treatment.
3 TECHNICAL EFFICACY: Stage 2.
研究表明，基于磁共振成像（MRI）的深度学习放射组学（DLR）有潜力评估神经胶质瘤级别；然而，其在预测胶质母细胞瘤（GBM）患者端粒酶逆转录酶（TERT）启动子突变状态中的作用仍不明确。
评估基于多参数MRI的放射组学中深度学习（DL）在术前识别GBM患者TERT启动子突变中的价值。
回顾性研究。
本研究共纳入274例异柠檬酸脱氢酶野生型GBM患者。训练队列和外部验证队列分别包括156例（年龄54.3±12.7岁；男性96例）和118例（年龄54.2±13.4岁；男性73例）患者。
场强/序列：本研究使用1.5-T和3.0-T扫描仪上的轴向对比增强T1加权自旋回波反转恢复序列（T1CE）、T1加权自旋回波反转恢复序列（T1WI）和T2加权自旋回波反转恢复序列（T2WI）。
对整个肿瘤区域（肿瘤核心和水肿）进行分割，并从术前预处理的多参数脑MRI图像-T1WI、T1CE和T2WI中提取放射组学和DL特征。开发并验证了基于DLR特征、临床特征和临床DLR（CDLR）列线图的模型，以识别TERT启动子突变状态。
应用曼-惠特尼U检验、皮尔逊检验、最小绝对收缩和选择算子以及逻辑回归分析进行特征选择和放射组学及DL特征的构建。P值<0.05时结果被认为具有统计学意义。
DLR特征在预测TERT启动子突变方面显示出最佳的判别能力，在训练队列和外部验证队列中的曲线下面积（AUC）分别为0.990和0.890。此外，DLR特征优于CDLR列线图（P=0.670），且在验证队列中显著优于临床模型。
基于多参数MRI的DLR特征在评估GBM患者TERT启动子突变方面表现出良好的性能，可为个体化治疗提供信息。
3级 技术效能：2级
