Stadlbauer Andreas, Heinz Gertraud, Marhold Franz, Meyer-Bäse Anke, Ganslandt Oliver, Buchfelder Michael, Oberndorfer Stefan
Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, 3100 St. Pölten, Austria.
Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, 91054 Erlangen, Germany.
Metabolites. 2022 Dec 14;12(12):1264. doi: 10.3390/metabo12121264.
Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO and mitoPO values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.
胶质母细胞瘤（GB）和脑转移瘤（BM）是成人中最常见的脑肿瘤类型。它们的治疗管理差异很大，快速可靠的初始特征对临床结果有重大影响。然而，由于GB和BM在传统磁共振成像（MRI）中外观非常相似，它们的鉴别仍然是当今临床神经肿瘤学中的一项重大挑战。新型代谢神经成像已被证明有助于提高诊断性能，但需要人工智能才能应用于临床常规。在此，我们研究了基于磁共振成像的氧代谢的放射组学特征（“氧代谢放射组学”）与深度卷积神经网络（CNN）的组合能否在临床环境中可靠地支持GB和BM的治疗前鉴别。在分类性能的所有参数方面，一个自行开发的结合了脑氧代谢率（CMRO）放射组学特征的一维CNN明显优于人工判读。与放射科医生相比，组织氧饱和度（mitoPO，即组织缺氧）的放射组学特征也显示出更好的诊断性能。有趣的是，GB和BM之间的定量CMRO和mitoPO值的均值和中位数均无显著差异。这表明，与均值或中位数比较相比，放射组学特征和深度学习算法的组合在类别区分方面更有效。氧代谢放射组学和深度神经网络为脑肿瘤表型提供了见解，可能具有重要的诊断意义，并有助于临床常规诊断。
