医学
随机森林
分级(工程)
人工智能
胶质瘤
多层感知器
逻辑回归
分割
机器学习
放射科
支持向量机
计算机科学
人工神经网络
磁共振成像
癌症研究
工程类
土木工程
作者
Feng-Ying Zhu,Yan Sun,Xiaoping Yin,T. Wang,Jintao Zhang,Li-Hong Xing,Xue Liu,Jia‐Ning Wang
标识
DOI:10.1016/j.clon.2023.08.001
摘要
Aims To build machine learning-based radiomics models to discriminate between high- (HGGs) and low-grade gliomas (LGGs) and to compare the effectiveness of three-dimensional arterial spin labelling (3D-ASL) to evaluate which is a better method. Materials and methods We retrospectively analysed the magnetic resonance imaging T1WI-enhanced images of 105 patients with gliomas that were pathologically confirmed in our hospital. We divided the patients into a training group and a verification group at a ratio of 8:2; 200 patients from the Brain Tumour Segmentation Challenge 2020 were selected as the test group for image segmentation, feature extraction and screening. We constructed models using multilayer perceptron (MLP), support vector machine, random forest and logistic regression and evaluated their predictive performance. We obtained the mean maximum relative cerebral blood flow (rCBFmax) value from 3D-ASL of 105 patients from the hospital to evaluate its efficacy in discriminating between HGGs and LGGs. Results In machine learning, the MLP classifier model exhibited the best performance in discriminating between HGGs and LGGs; the areas under the curve obtained by MLP and rCBFmax were 0.968 versus 0.815 (verification group) and 0.981 versus 0.815 (test group), respectively. The machine learning-based MLP classifier model performed better in discriminating between HGGs and LGGs than 3D-ASL. Conclusion In our study, we found that machine learning-based radiomics models and 3D-ASL were valuable in discriminating between HGGs and LGGs and between them, the machine learning-based MLP model had better diagnostic performance.
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