A Systematic Review of Risk Factors for Brain Metastases and Value of Prophylactic Cranial Irradiation in Non-Small Cell Lung Cancer

Sun, Dian-Shui,Hu, Li-Kuan,Cai, Ying,Li, Xiao-Mei,Ye, Lan,Hou, Hua-Ying,Wang, Cui-Hong,Jiang, Yu-Hua Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.3

Background: The incidence of brain metastases (BM) varies in patients with non-small cell lung cancer (NSCLC), calls into question the value of prophylactic cranial irradiation (PCI). It is possible that clinicopathologic characteristics are associated with the development of BM, but these have yet to be identified in detail. Thus, we conducted the present meta-analysis on risk factors for BM and the value of PCI in patients with NSCLC. Methods: Eligible data were extracted and the risk factors for BM and the value of PCI in patients with NSCLC were analyzed by calculating the pooled odds ratio (OR). Heterogeneity was detected using Q and I-squared statistics, and publication bias was tested by funnel plots and Egger's test. Results: Six randomized controlled trials with a focus on the value of PCI and 13 eligible studies with a focus on risk factors for BM were included. PCI significantly reduced the incidence of BM in patients with NSCLC (p=0.000, pooled OR=0.34, 95% confidence interval = 0.37-0.59). Compared with non-squamous cell carcinoma, squamous cell carcinoma was associated with a low incidence of BM in patients with NSCLC (p=0.000, pooled OR=0.47, 95% confidence interval =0.34-0.65). The funnel plot and Egger's test suggested that there was no publication bias in the current meta-analysis. Conclusions: This meta-analysis provides statistical evidence that compared with non-squamous cell carcinoma, squamous cell carcinoma can be used as a predictor for BM in patients with NSCLC, and PCI might reduce the incidence of BM in patients with NSCLC, but does not provide a survival benefit.

System Reliability Analysis of Rock Slope Stability Involving Correlated Failure Modes

Dian-Qing Li,Shui-Hua Jiang,Yi-Feng Chen,Chuang-Bing Zhou 대한토목학회 2011 KSCE JOURNAL OF CIVIL ENGINEERING Vol.15 No.8

This paper aims to propose a systematic quantitative method for system reliability evaluation of rock slope with plane failure involving multiple correlated failure modes. A probabilistic fault tree approach is presented to model system reliability of rock slope. An n-dimensional equivalent reliability method is employed to perform the system reliability analysis of the slope involving multiple correlated failure modes. Reliability sensitivity analyses at three different levels, namely, the single limit state function level, single failure mode level, and system reliability level, are carried out to study the effect of variables on reliability. An example is presented to demonstrate the validity and capability of the proposed approach. The results indicate that the system reliability of rock slope involving multiple correlated failure modes can be evaluated efficiently using the proposed approach. The system probability of failure is overestimated if the correlations between different failure modes are ignored. The relative importance of different failure modes to the system reliability can differ considerably. The sensitivity coefficients of basic random variables strongly depend on the selected sensitivity analysis level. The system reliability is sensitive to the location of the tension crack and the percentage of the tension crack filled with water.

A comparative study of three collocation point methods for odd order stochastic response surface method

Dian-Qing Li,Shui-Hua Jiang,Yong-Gang Cheng,Chuang-Bing Zhou 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.5

This paper aims to compare three collocation point methods associated with the odd order stochastic response surface method (SRSM) in a systematical and quantitative way. The SRSM with the Hermite polynomial chaos is briefly introduced first. Then, three collocation point methods, namely the point method, the root method and the without origin method underlying the odd order SRSMs are highlighted. Three examples are presented to demonstrate the accuracy and efficiency of the three methods. The results indicate that the condition that the Hermite polynomial information matrix evaluated at the collocation points has a full rank should be satisfied to yield reliability results with a sufficient accuracy. The point method and the without origin method are much more efficient than the root method, especially for the reliability problems involving a large number of random variables or requiring complex finite element analysis. The without origin method can also produce sufficiently accurate reliability results in comparison with the point and root methods. Therefore, the origin often used as a collocation point is not absolutely necessary. The odd order SRSMs with the point method and the without origin method are recommended for the reliability analysis due to their computational accuracy and efficiency. The order of SRSM has a significant influence on the results associated with the three collocation point methods. For normal random variables, the SRSM with an order equaling or exceeding the order of a performance function can produce reliability results with a sufficient accuracy. The order of SRSM should significantly exceed the order of the performance function involving strongly non-normal random variables.

Capabilities of stochastic response surface method and response surface method in reliability analysis

Shui-Hua Jiang,Dian-Qing Li,Chuang-Bing Zhou,Li-Min Zhang 국제구조공학회 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.49 No.1

The stochastic response surface method (SRSM) and the response surface method (RSM) are often used for structural reliability analysis, especially for reliability problems with implicit performance functions. This paper aims to compare these two methods in terms of fitting the performance function, accuracy and efficiency in estimating probability of failure as well as statistical moments of system output response. The computational procedures of two response surface methods are briefly introduced first. Then their capabilities are demonstrated and compared in detail through two examples. The results indicate that the probability of failure mainly reflects the accuracy of the response surface function (RSF) fitting the performance function in the vicinity of the design point, while the statistical moments of system output response reflect the accuracy of the RSF fitting the performance function in the entire space. In addition, the performance function can be well fitted by the SRSM with an optimal order polynomial chaos expansion both in the entire physical and in the independent standard normal spaces. However, it can be only well fitted by the RSM in the vicinity of the design point. For reliability problems involving random variables with approximate normal distributions, such as normal, lognormal, and Gumbel Max distributions, both the probability of failure and statistical moments of system output response can be accurately estimated by the SRSM, whereas the RSM can only produce the probability of failure with a reasonable accuracy.

A comparative study of three collocation point methods for odd order stochastic response surface method

Li, Dian-Qing,Jiang, Shui-Hua,Cheng, Yong-Gang,Zhou, Chuang-Bing Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.5

This paper aims to compare three collocation point methods associated with the odd order stochastic response surface method (SRSM) in a systematical and quantitative way. The SRSM with the Hermite polynomial chaos is briefly introduced first. Then, three collocation point methods, namely the point method, the root method and the without origin method underlying the odd order SRSMs are highlighted. Three examples are presented to demonstrate the accuracy and efficiency of the three methods. The results indicate that the condition that the Hermite polynomial information matrix evaluated at the collocation points has a full rank should be satisfied to yield reliability results with a sufficient accuracy. The point method and the without origin method are much more efficient than the root method, especially for the reliability problems involving a large number of random variables or requiring complex finite element analysis. The without origin method can also produce sufficiently accurate reliability results in comparison with the point and root methods. Therefore, the origin often used as a collocation point is not absolutely necessary. The odd order SRSMs with the point method and the without origin method are recommended for the reliability analysis due to their computational accuracy and efficiency. The order of SRSM has a significant influence on the results associated with the three collocation point methods. For normal random variables, the SRSM with an order equaling or exceeding the order of a performance function can produce reliability results with a sufficient accuracy. The order of SRSM should significantly exceed the order of the performance function involving strongly non-normal random variables.

Capabilities of stochastic response surface method and response surface method in reliability analysis

Jiang, Shui-Hua,Li, Dian-Qing,Zhou, Chuang-Bing,Zhang, Li-Min Techno-Press 2014 Structural Engineering and Mechanics, An Int'l Jou Vol.49 No.1

The stochastic response surface method (SRSM) and the response surface method (RSM) are often used for structural reliability analysis, especially for reliability problems with implicit performance functions. This paper aims to compare these two methods in terms of fitting the performance function, accuracy and efficiency in estimating probability of failure as well as statistical moments of system output response. The computational procedures of two response surface methods are briefly introduced first. Then their capabilities are demonstrated and compared in detail through two examples. The results indicate that the probability of failure mainly reflects the accuracy of the response surface function (RSF) fitting the performance function in the vicinity of the design point, while the statistical moments of system output response reflect the accuracy of the RSF fitting the performance function in the entire space. In addition, the performance function can be well fitted by the SRSM with an optimal order polynomial chaos expansion both in the entire physical and in the independent standard normal spaces. However, it can be only well fitted by the RSM in the vicinity of the design point. For reliability problems involving random variables with approximate normal distributions, such as normal, lognormal, and Gumbel Max distributions, both the probability of failure and statistical moments of system output response can be accurately estimated by the SRSM, whereas the RSM can only produce the probability of failure with a reasonable accuracy.