Cho Hwan-Ho, Lee Seung-Hak, Kim Jonghoon, Park Hyunjin
Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.
PeerJ. 2018 Nov 22;6:e5982. doi: 10.7717/peerj.5982. eCollection 2018.
Grading of gliomas is critical information related to prognosis and survival. We aimed to apply a radiomics approach using various machine learning classifiers to determine the glioma grading.
We considered 285 (high grade = 210, low grade = 75) cases obtained from the Brain Tumor Segmentation 2017 Challenge. Manual annotations of enhancing tumors, non-enhancing tumors, necrosis, and edema were provided by the database. Each case was multi-modal with T1-weighted, T1-contrast enhanced, T2-weighted, and FLAIR images. A five-fold cross validation was adopted to separate the training and test data. A total of 468 radiomics features were calculated for three types of regions of interest. The minimum redundancy maximum relevance algorithm was used to select features useful for classifying glioma grades in the training cohort. The selected features were used to build three classifier models of logistics, support vector machines, and random forest classifiers. The classification performance of the models was measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve. The trained classifier models were applied to the test cohort.
Five significant features were selected for the machine learning classifiers and the three classifiers showed an average AUC of 0.9400 for training cohorts and 0.9030 (logistic regression 0.9010, support vector machine 0.8866, and random forest 0.9213) for test cohorts.
Glioma grading could be accurately determined using machine learning and feature selection techniques in conjunction with a radiomics approach. The results of our study might contribute to high-throughput computer aided diagnosis system for gliomas.
胶质瘤分级是与预后和生存相关的关键信息。我们旨在应用一种放射组学方法，使用各种机器学习分类器来确定胶质瘤分级。
我们纳入了从2017年脑肿瘤分割挑战赛中获得的285例病例（高级别=210例，低级别=75例）。数据库提供了强化肿瘤、非强化肿瘤、坏死和水肿的手动标注。每个病例均有多模态的T1加权、T1增强、T2加权和FLAIR图像。采用五折交叉验证来分离训练和测试数据。针对三种类型的感兴趣区域计算了总共468个放射组学特征。使用最小冗余最大相关算法在训练队列中选择对胶质瘤分级分类有用的特征。所选特征用于构建逻辑回归、支持向量机和随机森林分类器这三种分类器模型。在训练队列中使用准确率、灵敏度、特异性以及接收器操作特征曲线的曲线下面积（AUC）来衡量模型的分类性能。将训练好的分类器模型应用于测试队列。
为机器学习分类器选择了五个显著特征，这三种分类器在训练队列中的平均AUC为0.9400，在测试队列中的平均AUC为0.9030（逻辑回归为0.9010，支持向量机为0.8866，随机森林为0.9213）。
结合放射组学方法，使用机器学习和特征选择技术可以准确确定胶质瘤分级。我们的研究结果可能有助于开发用于胶质瘤诊断的高通量计算机辅助诊断系统。
