论文标题
使用磁共振成像的深度卷积神经网络用于胶质瘤的分子亚型
Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas Using Magnetic Resonance Imaging
论文作者
论文摘要
胶质瘤分子亚型的知识可以为量身定制的疗法提供有价值的信息。这项研究旨在根据世界卫生组织在2016年宣布的新分类法进行研究，研究深卷卷神经网络（DCNNS）与放射成像数据进行非侵染性神经胶质瘤亚型。方法：DCNN模型是为基于五个Glioma subtypes的五个Glioma模型而开发的。该模型使用了三个平行的，重量分担的深层残余学习网络来处理2.5维三峰MRI数据的输入，包括T1加权，T1加权和对比度增强和T2加权图像。收集了一个包括1,016名实际患者的数据集，以评估开发的DCNN模型。通过接收器操作特征分析通过曲线（AUC）下的区域评估预测性能。为了进行比较，还评估了基于放射线学方法的性能。结果：分层分类范式中的四个分类任务的DCNN模型的AUC分别为0.89、0.89、0.85和0.66，而放射学方法的0.85、0.75、0.67和0.59相比。结论：结果表明，鉴于足够的，非平衡的训练数据，开发的DCNN模型可以预测具有有希望的性能的神经胶质瘤亚型。
Knowledge of molecular subtypes of gliomas can provide valuable information for tailored therapies. This study aimed to investigate the use of deep convolutional neural networks (DCNNs) for noninvasive glioma subtyping with radiological imaging data according to the new taxonomy announced by the World Health Organization in 2016. Methods: A DCNN model was developed for the prediction of the five glioma subtypes based on a hierarchical classification paradigm. This model used three parallel, weight-sharing, deep residual learning networks to process 2.5-dimensional input of trimodal MRI data, including T1-weighted, T1-weighted with contrast enhancement, and T2-weighted images. A data set comprising 1,016 real patients was collected for evaluation of the developed DCNN model. The predictive performance was evaluated via the area under the curve (AUC) from the receiver operating characteristic analysis. For comparison, the performance of a radiomics-based approach was also evaluated. Results: The AUCs of the DCNN model for the four classification tasks in the hierarchical classification paradigm were 0.89, 0.89, 0.85, and 0.66, respectively, as compared to 0.85, 0.75, 0.67, and 0.59 of the radiomics approach. Conclusion: The results showed that the developed DCNN model can predict glioma subtypes with promising performance, given sufficient, non-ill-balanced training data.
