Two individual features achieved log-rank P values of 0.003 and P value <0.001 respectively in the validation data set. The other four individual features failed to stratify patients into high-risk and low-risk groups in the val- idation data set. The multi-feature signature was successful to predict the OS of patients in the validation data set and performed better than any individual feature. From the statistical perspective, nonsignificant association with survival does not mean less importance. On the other hand, multivariate model is statistically robust in survival analysis26. Moreover, the intra-tumor genetic heterogeneity suggests that tumor subregions could be genetically different and may comprise multiple subclones. This could be better reflected by multiple high-order deep fea- tures extracted from multi-subregions in multi-modalities rather than individual feature. Similar to the genomic studies of exploring biomarkers from high-throughput genomic data, it is also regarded as a common“-omics” approach to construct a multi-factor radiomics signature for outcome prediction. 解释
时间: 2024-04-27 15:22:12 浏览: 6
这段话介绍了一项研究,该研究使用了多个特征来预测患者的生存期,其中有两个特征在验证数据集中获得了显著的统计学结果,但其他四个特征则没有。然而,使用多个特征的组合可以更准确地预测患者的生存期,比单个特征更有效。此外,研究者指出,即使在单个特征的分析中没有发现显著的生存期关联,也不能说明该特征不重要。从统计学角度来看,使用多元模型可以更加稳健地进行生存分析。此外,肿瘤内部的遗传异质性意味着肿瘤不同部位的遗传信息可能不同,因此使用多种模态的多个高阶深度特征可以更好地反映肿瘤的内部异质性。与高通量基因组学研究探索生物标志物类似,构建多因素放射组学签名也是一种常见的“组学”方法,可用于预测患者的生存期。
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The radiomics signature achieved a C-Index of 0.731 (95% confidence intervals [CI]: 0.645, 0.817) for the discovery data set, and 0.710 (95% CI: 0.588, 0.932) for the independent validation data set, demonstrating the predictive performance of the model. Based on the radiomics score of patients in the discov- ery data set, the optimal cutoff calculated by the X-tile plot was 0.1343235, as shown in Supplementary Figure 2. Then, patients in both the discovery and validation data sets were stratified into low-risk (Rad-score < 0.1343235) and high-risk (Rad-score>0.1343235) groups, as shown in Fig. 3. The significant association of the radiomics signature with OS was shown in discovery data set (P < 0.001, hazard ratio [HR] = 5.042, 95% CI: 2.624, 9.689), and confirmed in the validation data set (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The OS in the low-risk and high-risk groups in the discovery and validation data sets are listed in Supplementary Table 4. 解释
这段话描述了一项研究,研究者使用了放射学特征学(radiomics)来预测患者的生存情况。在发现数据集中,这个放射学特征学模型的C-Index为0.731,在独立验证数据集中为0.710,表明了该模型的预测性能。通过X-tile图计算得到的放射学分数的最优阈值为0.1343235。根据这个阈值,研究者将患者分为低风险组和高风险组,结果显示放射学特征学与患者的生存率显著相关,无论是在发现数据集中还是在验证数据集中,都表现出较高的预测力。
Exception in thread "Timer-0" org.eclipse.swt.SWTException: Invalid thread access
This exception is thrown when an SWT operation is performed from a non-UI thread. SWT is a user interface toolkit used in Java applications, and it requires all UI-related operations to be performed from the UI thread.
To fix this exception, you need to ensure that any SWT-related operations are performed from the UI thread. This can be achieved by using the `Display.asyncExec()` or `Display.syncExec()` methods, which execute a Runnable on the UI thread.
For example, if you have a Timer that updates a SWT component, you can wrap the update code in a `Display.asyncExec()` block:
```
Timer timer = new Timer();
timer.schedule(new TimerTask() {
@Override
public void run() {
Display.getDefault().asyncExec(new Runnable() {
public void run() {
// Update SWT component here
}
});
}
}, 0, 1000);
```
This ensures that the update code is executed on the UI thread and avoids the Invalid thread access exception.