Stadlbauer Andreas, Marhold Franz, Oberndorfer Stefan, Heinz Gertraud, Buchfelder Michael, Kinfe Thomas M, Meyer-Bäse Anke
Institute of Medical Radiology, University Clinic St. Pölten, Karl Landsteiner University of Health Sciences, A-3100 St. Pölten, Austria.
Department of Neurosurgery, Friedrich-Alexander University (FAU) Erlangen-Nürnberg, D-91054 Erlangen, Germany.
Cancers (Basel). 2022 May 10;14(10):2363. doi: 10.3390/cancers14102363.
The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks.
对比增强型脑肿瘤的精确初始特征描述对临床结果具有重大影响。已开发出各种新型神经成像方法，以提高传统磁共振成像（cMRI）的特异性，但同时也增加了数据分析的复杂性。人工智能为在临床环境中应对这一挑战提供了新的选择。在此，我们研究了应用于来自高级MRI（advMRI）和生理MRI（phyMRI；即放射生理组学）的高维放射组学特征面板的多类机器学习（ML）算法是否能够可靠地对对比增强型脑肿瘤进行分类。最近开发的phyMRI技术能够对微血管结构、新生血管形成、氧代谢和组织缺氧进行定量评估。一个由167名患有五种最常见脑肿瘤实体之一（胶质母细胞瘤、间变性胶质瘤、脑膜瘤、原发性中枢神经系统淋巴瘤或脑转移瘤）的患者组成的训练队列，结合九种常见的ML算法，用于开发总共135个分类器。使用十折交叉验证和一个独立测试队列研究多类分类性能。结合advMRI和phyMRI数据的自适应增强和随机森林在准确性（0.875对0.850）、精确率（0.862对0.798）、F值（0.774对0.740）、曲线下面积（0.886对0.813）和分类错误率（5对6）方面优于人工判读。然而，放射科医生表现出更高的敏感性（0.767对0.750）和特异性（0.925对0.902）。我们证明基于ML的放射生理组学在对比增强型脑肿瘤的临床常规诊断中可能会有所帮助；然而，数据预处理需要大量的时间和工作，这需要纳入深度神经网络。
