Department of Neurosurgery, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310009, China.
Department of Neurosurgery, Clinical Research Center for Neurological Diseases of Zhejiang Province, Hangzhou 310009, China.
Br J Radiol. 2024 Nov 1;97(1163):1806-1815. doi: 10.1093/bjr/tqae146.
Telomerase reverse transcriptase promoter (pTERT) mutation status plays a key role in making decisions and predicting prognoses for patients with World Health Organization (WHO) grade IV glioma. This study was conducted to assess the value of diffusion-weighted imaging (DWI) for predicting pTERT mutation status in WHO grade IV glioma.
MRI data and molecular information were obtained for 266 patients with WHO grade IV glioma at the hospital and divided into training and validation sets. The ratio of training to validation set was approximately 10:3. We trained the same residual convolutional neural network (ResNet) for each MR modality, including structural MRIs (T1-weighted, T2-weighted, and contrast-enhanced T1-weighted) and DWI*, to compare the predictive capacities between DWI and conventional structural MRI. We also explored the effects of different regions of interest on pTERT mutation status prediction outcomes.
Structural MRI modalities poorly predicted the pTERT mutation status (accuracy = 51%-54%; area under the curve [AUC]=0.545-0.571), whereas DWI combined with its apparent diffusive coefficient maps yielded the best predictive performance (accuracy = 85.2%, AUC = 0.934). Including the radiological and clinical characteristics did not further improve the performance for predicting pTERT mutation status. The entire tumour volume yielded the best prediction performance.
DWI technology shows promising potential for predicting pTERT mutations in WHO grade IV glioma and should be included in the MRI protocol for WHO grade IV glioma in clinical practice.
This is the first large-scale model study to validate the predictive value of DWI for pTERT in WHO grade IV glioma.
端粒酶逆转录酶启动子（pTERT）突变状态在决定和预测世界卫生组织（WHO）IV 级胶质瘤患者的预后方面起着关键作用。本研究旨在评估弥散加权成像（DWI）在预测 WHO 级 IV 级胶质瘤中 pTERT 突变状态的价值。
在医院获得了 266 例 WHO 级 IV 级胶质瘤患者的 MRI 数据和分子信息，并将其分为训练集和验证集。训练集与验证集的比例约为 10:3。我们为每种 MR 模态（包括结构 MRI [T1 加权、T2 加权和对比增强 T1 加权]和 DWI*）训练相同的残差卷积神经网络（ResNet），以比较 DWI 和传统结构 MRI 之间的预测能力。我们还探讨了不同感兴趣区域对 pTERT 突变状态预测结果的影响。
结构 MRI 模态对 pTERT 突变状态的预测效果较差（准确率=51%-54%；曲线下面积[AUC]=0.545-0.571），而 DWI 与其表观弥散系数图相结合则具有最佳的预测性能（准确率=85.2%，AUC=0.934）。纳入影像学和临床特征并没有进一步提高预测 pTERT 突变状态的性能。整个肿瘤体积的预测性能最佳。
DWI 技术在预测 WHO 级 IV 级胶质瘤中的 pTERT 突变方面具有广阔的应用前景，在临床实践中应将其纳入 WHO 级 IV 级胶质瘤的 MRI 方案中。
这是第一个验证 DWI 对 WHO 级 IV 级胶质瘤中 pTERT 预测价值的大规模模型研究。
