基于手工制作和深度学习的放射组学模型可区分胶质母细胞瘤和脑转移瘤。
Handcrafted and Deep Learning-Based Radiomic Models Can Distinguish GBM from Brain Metastasis.
作者信息
Liu Zhiyuan, Jiang Zekun, Meng Li, Yang Jun, Liu Ying, Zhang Yingying, Peng Haiqin, Li Jiahui, Xiao Gang, Zhang Zijian, Zhou Rongrong
机构信息
Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China.
Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China.
出版信息
J Oncol. 2021 Jun 3;2021:5518717. doi: 10.1155/2021/5518717. eCollection 2021.
OBJECTIVE
The purpose of this study was to investigate the feasibility of applying handcrafted radiomics (HCR) and deep learning-based radiomics (DLR) for the accurate preoperative classification of glioblastoma (GBM) and solitary brain metastasis (BM).
METHODS
A retrospective analysis of the magnetic resonance imaging (MRI) data of 140 patients (110 in the training dataset and 30 in the test dataset) with GBM and 128 patients (98 in the training dataset and 30 in the test dataset) with BM confirmed by surgical pathology was performed. The regions of interest (ROIs) on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and contrast-enhanced T1WI (T1CE) were drawn manually, and then, HCR and DLR analyses were performed. On this basis, different machine learning algorithms were implemented and compared to find the optimal modeling method. The final classifiers were identified and validated for different MRI modalities using HCR features and HCR + DLR features. By analyzing the receiver operating characteristic (ROC) curve, the area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the predictive efficacy of different methods.
RESULTS
In multiclassifier modeling, random forest modeling showed the best distinguishing performance among all MRI modalities. HCR models already showed good results for distinguishing between the two types of brain tumors in the test dataset (T1WI, AUC = 0.86; T2WI, AUC = 0.76; T1CE, AUC = 0.93). By adding DLR features, all AUCs showed significant improvement (T1WI, AUC = 0.87; T2WI, AUC = 0.80; T1CE, AUC = 0.97; < 0.05). The T1CE-based radiomic model showed the best classification performance (AUC = 0.99 in the training dataset and AUC = 0.97 in the test dataset), surpassing the other MRI modalities ( < 0.05). The multimodality radiomic model also showed robust performance (AUC = 1 in the training dataset and AUC = 0.84 in the test dataset).
CONCLUSION
Machine learning models using MRI radiomic features can help distinguish GBM from BM effectively, especially the combination of HCR and DLR features.
目的
本研究旨在探讨应用手工制作的放射组学（HCR）和基于深度学习的放射组学（DLR）对胶质母细胞瘤（GBM）和孤立性脑转移瘤（BM）进行准确术前分类的可行性。
方法
对140例经手术病理证实为GBM的患者（训练数据集110例，测试数据集30例）和128例经手术病理证实为BM的患者（训练数据集98例，测试数据集30例）的磁共振成像（MRI）数据进行回顾性分析。在T1加权成像（T1WI）、T2加权成像（T2WI）和对比增强T1WI（T1CE）上手动绘制感兴趣区域（ROI），然后进行HCR和DLR分析。在此基础上，实施并比较不同的机器学习算法，以找到最佳建模方法。使用HCR特征和HCR + DLR特征对不同MRI模态的最终分类器进行识别和验证。通过分析受试者工作特征（ROC）曲线，计算曲线下面积（AUC）、准确率、灵敏度和特异度，以评估不同方法的预测效能。
结果
在多分类器建模中，随机森林建模在所有MRI模态中表现出最佳的区分性能。HCR模型在测试数据集中对两种类型脑肿瘤的区分已经显示出良好的结果（T1WI，AUC = 0.86；T2WI，AUC = 0.76；T1CE，AUC = 0.93）。通过添加DLR特征，所有AUC均有显著提高（T1WI，AUC = 0.87；T2WI，AUC = 0.80；T1CE，AUC = 0.97；<0.05）。基于T1CE的放射组学模型表现出最佳的分类性能（训练数据集中AUC = 0.99，测试数据集中AUC = 0.97），超过其他MRI模态（<0.05）。多模态放射组学模型也表现出稳健的性能（训练数据集中AUC = 1，测试数据集中AUC = 0.84）。
结论
使用MRI放射组学特征的机器学习模型可以有效帮助区分GBM和BM，尤其是HCR和DLR特征的组合。
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