In our study the proposed radiomics signature performed better than traditional risk factors such as age and KPS. None of these clinical factors successfully stratified patients into groups with different prognostic risks. After combining the radiomics signature with clinical factors into a Cox regression model, the predictive power improved with C-index of 0.739 in validation data set. According to the radiomics signature and the two clinical risk factors, we drew a nomogram that can visually predict the probability of survival. According to the calibra- tion curve we can see that our nomogram had good predictive performance. 解释

这段话是在讲述一项医学研究，研究人员使用了一种叫做“放射组学签名”的方法来预测患者的预后风险，这种方法比传统的风险因素如年龄和身体状况评分（KPS）更有效。经过将放射组学签名与临床因素结合起来，研究人员得到了一种名为“诺模图”的预测模型，可以直观地预测患者的生存概率。通过校准曲线可以看出，这个诺模图的预测性能很好。总的来说，这项研究表明，放射组学签名可以作为一种有效的预测方法，可以帮助医生更准确地评估患者的预后风险。

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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。

检查下列语句的语法和拼写问题。Traditional network security situation prediction methods depend on the accuracy of historical situation value. Moreover, there are differences in correlation and importance among various network security factors. In order to solve these problems, a combined prediction model based on the temporal convolution attention network (TCAN) and bi-directional gate recurrent unit (BiGRU) network optimized by singular spectrum analysis (SSA) and improved quantum particle swarm optimization algorithm (IQPSO) was proposed. This model was first decomposed and reconstructed into a series of subsequences through the SSA of network security situation data. Next, a prediction model of TCAN-BiGRU was established for each subsequence, respectively. The TCN with relatively simple structure was used in the TCAN to extract features from the data. Besides, the improved channel attention mechanism (CAM) was used to extract important feature information from TCN. Afterwards, the before-after status of the learning situation value of the BiGRU neural network was used to extract more feature information from sequences for prediction. Meanwhile, an improved IQPSO was proposed to optimize the hyper-parameter of the BiGRU neural network. Finally, the prediction results of subsequence were superimposed to obtain the final predicted value. In the experiment, on the one hand, the IQPSO was compared with other optimization algorithms; and the results showed that the IQPSO has better optimization performance; on the other hand, the comparison with traditional prediction methods was performed through the simulation experiment and the established prediction model; and the results showed that the combined prediction model established has higher prediction accuracy.

我们可以看到，本文提出的综合预测模型基于时间卷积注意网络（TCAN）和双向门递归单元（BiGRU）网络，并通过奇异谱分析（SSA）和改进的量子粒子群优化算法（IQPSO）进行优化。实验表明，该模型具有更高的预测精度。