Deep learning-based radiomics (DLR) was developed to extract deep information from multiple modalities of magnetic resonance (MR) images. The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma. A modified convolutional neural network (CNN) structure with 6 convolutional layers and a fully connected layer with 4096 neurons was used to segment tumors. Instead of calculating image features from segmented images, as typically performed for normal radiomics approaches, image features were obtained by normalizing the information of the last convolutional layers of the CNN. Fisher vector was used to encode the CNN features from image slices of different sizes. High-throughput features with dimensionality greater than 1.6*104 were obtained from the CNN. Paired t-tests and F-scores were used to select CNN features that were able to discriminate IDH1. With the same dataset, the area under the operating characteristic curve (AUC) of the normal radiomics method was 86% for IDH1 estimation, whereas for DLR the AUC was 92%. The AUC of IDH1 estimation was further improved to 95% using DLR based on multiple-modality MR images. DLR could be a powerful way to extract deep information from medical images. 解释

这段文本介绍了一种基于深度学习的影像分析方法，即Deep learning-based radiomics (DLR)。该方法使用了修改后的卷积神经网络(CNN)结构，通过对磁共振(MR)影像进行多模态分析，提取更深层次的信息。该研究使用了151例低级别胶质瘤患者的数据集，通过对肿瘤进行分割，得到CNN的最后卷积层信息，再利用Fisher向量进行编码，得到维度高达1.6*104的高通量特征。该研究表明，相比于传统的放射学方法，DLR方法在预测异柠酸脱氢酶1(IDH1)突变状态时，具有更高的准确性，其AUC值达到了92%~95%。这些发现表明，DLR方法可以有效地从医学影像中提取更深层次的信息，对肿瘤的诊断和治疗具有潜在的帮助。

相关问题

To overcome the shortcomings of radiomics methods, we developed a more advanced method, called deep learning-based radiomics (DLR). DLR obtains radiomics features by normalizing the information from a deep neural network designed for image segmentation. The main assumption of DLR is that once the image has been segmented accurately by the deep neural network, all the information about the segmented region will have already been installed in the network. Unlike current radiomics methods, in DLR, the high-throughput image features are directly extracted from the deep neural network. Because DLR does not involve extra feature extrac- tion operations, no extra errors will be introduced into the radiomics analysis because of feature calculations. The effectiveness of features is related only to the quality of segmentation. If the tumor has been segmented precisely, the accuracy and effectiveness of the image features can be guaranteed 解释

这段话提到了一个新的医学影像分析方法，叫做基于深度学习的放射组学（DLR）。与现有的放射组学方法不同，DLR直接从深度神经网络中提取高通量的图像特征，而不需要进行额外的特征提取操作，从而避免了因特征计算而引入额外的误差。DLR的主要假设是，一旦图像被深度神经网络准确地分割出来，所有与分割区域相关的信息都已经被嵌入到网络中。因此，DLR的特征有效性与分割的质量密切相关。如果肿瘤被准确地分割了，那么图像特征的准确性和有效性就能得到保证。

In conclusion, we have proposed a six-deep-feature radiomics signature that have the potential to be an imag- ing biomarker for prediction of the OS in patients with GBM. It was demonstrated that the deep learning method can be incorporated into the state-of-the-art radiomics model to achieve a better performance. The proposed signature predicted the OS in GBM patients with better performance compared with conventional factors such as age and KPS. A nomogram was proposed for prediction of the probability of survival. Despite the limitations, the proposed radiomics model has the potential to facilitate the preoperative care of patients with GBM. 解释

这段话总结了这项研究的主要发现和贡献。研究提出了一个由六个深度特征组成的放射组学标记，具有成为GBM患者OS预测的成像生物标志物的潜力。研究表明，深度学习方法可以融入最新的放射组学模型，以实现更好的性能。与年龄和KPS等传统因素相比，所提出的标记对GBM患者的OS预测具有更好的性能。研究提出了一个预测生存概率的数学模型。尽管存在一些限制，但所提出的放射组学模型有望促进GBM患者的术前护理。