文章：

基于机器学习利用氧代谢与新生血管生理代谢MRI预测胶质瘤IDH基因突变状态（一项双中心研究）

Machine Learning-Based Prediction of GliomaIDHGene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study)

原文发布日期：8 March 2024

DOI: 10.3390/cancers16061102

类型: Article

开放获取: 是

英文摘要：

The mutational status of the isocitrate dehydrogenase (IDH) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of oxygen metabolism and tissue hypoxia as well as associated neovascularization and microvascular architecture. However, evaluating such complex neuroimaging data requires computational support. Traditional machine learning algorithms and simple deep learning models were trained with radiomic features from clinical MRI (cMRI) or physio-metabolic MRI data. A total of 215 patients (first center: 166 participants + 16 participants for independent internal testing of the algorithms versus second site: 33 participants for independent external testing) were enrolled using two different physio-metabolic MRI protocols. The algorithms trained with physio-metabolic data demonstrated the best classification performance in independent internal testing: precision, 91.7%; accuracy, 87.5%; area under the receiver operating curve (AUROC), 0.979. In external testing, traditional machine learning models trained with cMRI data exhibited the bestIDHclassification results: precision, 84.9%; accuracy, 81.8%; and AUROC, 0.879. The poor performance for the physio-metabolic MRI approach appears to be explainable by site-dependent differences in data acquisition methodologies. The physio-metabolic MRI approach potentially supports reliable classification ofIDHgene status in the presurgical stage of glioma patients. However, non-standardized protocols limit the level of evidence and underlie the need for a reproducible framework of data acquisition techniques.

摘要翻译： 

异柠檬酸脱氢酶（IDH）基因的突变状态在胶质瘤患者的治疗中起着关键作用，因其已知影响与胶质瘤相关的能量代谢通路。生理代谢磁共振成像（MRI）能够无创分析氧代谢和组织缺氧，以及相关的新生血管形成和微血管结构。然而，评估此类复杂的神经影像数据需要计算支持。传统机器学习算法和简单深度学习模型使用临床MRI（cMRI）或生理代谢MRI数据的影像组学特征进行训练。研究共纳入215名患者（第一中心：166名参与者+16名参与者用于算法独立内部测试；第二中心：33名参与者用于独立外部测试），采用两种不同的生理代谢MRI方案。使用生理代谢数据训练的算法在独立内部测试中表现出最佳分类性能：精确度91.7%、准确度87.5%、受试者工作特征曲线下面积（AUROC）0.979。在外部测试中，使用cMRI数据训练的传统机器学习模型展现出最优IDH分类结果：精确度84.9%、准确度81.8%、AUROC 0.879。生理代谢MRI方法表现不佳的原因可能源于不同中心数据采集方法的差异。生理代谢MRI方法有望在胶质瘤患者术前阶段为IDH基因状态的可靠分类提供支持，但非标准化的采集方案限制了证据等级，凸显了建立可重复数据采集技术框架的必要性。

原文链接：

Machine Learning-Based Prediction of GliomaIDHGene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study)