***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Background Glioblastoma Multiforme ( GBM ) is the most common primary malignant tumor of the central nervous system . St and ard of care includes maximal resection followed by chemoradiotherapy . Tumors need adequate perfusion and neovascularization to maintain oxygenation and for removal of wastes . Vascular endothelial growth factor ( VEGF ) is a well characterized pro-angiogenic factor . We hypothesized that the increases in urinary VEGF levels would occur early in the course of tumor recurrence or progression . We examine the feasibility of collecting and analyzing urinary VEGF levels in a prospect i ve , multi-institutional trial ( Radiation Therapy Oncology Group , RTOG , 0611 ) as well as the role of VEGF as a marker of tumor recurrence . Method We evaluated  VEGF levels in urine specimens collected post-operatively , at the conclusion of radiation therapy ( RT ) and one month following RT .  Urinary VEGF levels were correlated with tumor progression at one year . VEGF levels were measured by enzyme-linked immunosorbant assay in urine specimens and normalized to urinary creatinine levels . Sample size was determined based on a 50 % 1-year recurrence rate . With a sensitivity and specificity of 80 % , the expected 95 % confidence interval was ( 0.69 , 0.91 ) with 100 patients . A failure was defined as documented disease progression , recurrence or death before one year . Results 202 patients were enrolled between February-2006 and October-2007 . Four patients were ineligible as they did not receive RT . Of the remaining 198 patients , 128 had all three sample s collected . In this group , 35 patients ( 27.3 % ) did not progress , 89 ( 69.5 % ) had progression and 4 ( 3.1 % ) died without evidence of progression . Median VEGF levels at baseline were 52.9 pg/mg Cr ( range 0.2- 15,034.4 ) ; on the last day of RT , 56.6 ( range 0–2,377.1 ) ; and at one month follow-up , 70.0 ( range 0.1 - 1813.2 ) . In patients without progression at 1-year , both baseline VEGF level and end of RT VEGF level were lower than those of patients who progressed : 40.3 ( range 0.2 - 350.8 ) vs. 59.7 ( range 1.3 - 15,034.4 ) and 41.8 ( range 0–356.8 ) vs. 69.7 ( range 0–2,377.1 ) , respectively . This did not reach statistical significance . Comparison of the change in VEGF levels between the end of RT and one month following RT , demonstrated no significant difference in the proportions of progressors or non-progressors at 1-year for either the VEGF increased or VEGF decreased groups . Conclusion Urine can be collected and analyzed in a prospect i ve , multi-institutional trial . In this study of patients with GBM a change in urinary VEGF levels between the last day of RT and the one month following RT did not predict for tumor progression by one year BACKGROUND Measures of tumor vascularity and hypoxia have been correlated with glioma grade and outcome .  Dynamic contrast-enhanced ( DCE ) MRI can noninvasively map tumor blood flow , vascularity , and permeability . In this prospect i ve observational cohort pilot study , preoperative imaging was correlated with molecular markers of hypoxia , vascularity , proliferation , and progression-free and overall patient survival . METHODS Pharmacokinetic modeling methods were used to generate maps of tumor blood flow , extraction fraction , permeability-surface area product , transfer constant , washout rate , interstitial volume , blood volume , capillary transit time , and capillary heterogeneity from preoperative DCE-MRI data in human glioma patients . Tissue was obtained from areas of peritumoral edema , active tumor , hypoxic penumbra , and necrotic core and evaluated for vascularity , proliferation , and expression of hypoxia-regulated molecules . DCE-MRI parameter values were correlated with hypoxia-regulated protein expression at tissue sample sites . RESULTS Patient survival correlated with DCE parameters in 2 cases : capillary heterogeneity in active tumor and interstitial volume in areas of peritumoral edema . Statistically significant correlations were observed between several DCE parameters and tissue markers . In addition , MIB-1 index was predictive of overall survival ( P = .044 ) and correlated with vascular endothelial growth factor expression in hypoxic penumbra ( r = 0.7933 , P = .0071 ) and peritumoral edema ( r = 0.4546 ) . Increased microvessel density correlated with worse patient outcome ( P = .026 ) . CONCLUSIONS Our findings suggest that DCE-MRI may facilitate noninvasive preoperative predictions of areas of tumor with increased hypoxia and proliferation . Both imaging and hypoxia biomarkers are predictive of patient outcome . This has the potential to allow unprecedented prognostic decisions and to guide therapies to specific tumor areas PURPOSE The combination of a vascular endothelial growth factor ( VEGF ) -neutralizing antibody , bevacizumab , and irinotecan is associated with high radiographic response rates and improved survival outcomes in patients with recurrent malignant gliomas . The aim of these retrospective studies was to evaluate tumor vascularity and expression of components of the VEGF pathway and hypoxic responses as predictive markers for radiographic response and survival benefit from the bevacizumab and irinotecan therapy . PATIENTS AND METHODS In a phase II trial , 60 patients with recurrent malignant astrocytomas were treated with bevacizumab and irinotecan . Tumor specimens collected at the time of diagnosis were available for further pathologic studies in 45 patients ( 75 % ) . VEGF , VEGF receptor-2 , CD31 , hypoxia-inducible carbonic anhydrase 9 ( CA9 ) , and hypoxia-inducible factor-2alpha were semiquantitatively assessed by immunohistochemistry . Radiographic response and survival outcomes were correlated with these angiogenic and hypoxic markers . RESULTS Of 45 patients , 27 patients had glioblastoma multiforme , and 18 patients had anaplastic astrocytoma . Twenty-six patients ( 58 % ) had at least partial radiographic response . High VEGF expression was associated with increased likelihood of radiographic response ( P = .024 ) but not survival benefit . Survival analysis revealed that high CA9 expression was associated with poor survival outcome ( P = .016 ) . CONCLUSION In this patient cohort , tumor expression levels of VEGF , the molecular target of bevacizumab , were associated with radiographic response , and the upstream promoter of angiogenesis , hypoxia , determined survival outcome , as measured from treatment initiation . Validation in a larger clinical trial is warranted A quantitative systematic review , or meta- analysis , uses statistical methods to combine the results of multiple studies . Meta-analyses have been done for systematic review s of therapeutic trials , diagnostic test evaluations , and epidemiologic studies . Although the statistical methods involved may at first appear to be mathematically complex , their purpose is simple : They are trying to answer four basic questions . Are the results of the different studies similar ? To the extent that they are similar , what is the best overall estimate ? How precise and robust is this estimate ? Finally , can dissimilarities be explained ? This article provides some guidance in underst and ing the key technical aspects of the quantitative approach to these questions . We have avoided using equations and statistical notations ; interested readers will find implementations of the described methods in the listed references . We focus here on the quantitative synthesis of reports of r and omized , controlled , therapeutic trials because far more meta-analyses on therapeutic studies than on other types of studies have been published . For practical reasons , we present a stepwise description of the tasks that are performed when statistical methods are used to combine data . These tasks are 1 ) deciding whether to combine data and defining what to combine , 2 ) evaluating the statistical heterogeneity of the data , 3 ) estimating a common effect , 4 ) exploring and explaining heterogeneity , 5 ) assessing the potential for bias , and 6 ) presenting the results . Deciding Whether To Combine Data and Defining What To Combine By the time one performs a quantitative synthesis , certain decisions should already have been made about the formulation of the question and the selection of included studies . These topics were discussed in two previous articles in this series [ 1 , 2 ] . Statistical tests can not compensate for lack of common sense , clinical acumen , and biological plausibility in the design of the protocol of a meta- analysis . Thus , a reader of a systematic review should always address these issues before evaluating the statistical methods that have been used and the results that have been generated . Combining poor- quality data , overly biased data , or data that do not make sense can easily produce unreliable results . The data to be combined in a meta- analysis are usually either binary or continuous . Binary data involve a yes/no categorization ( for example , death or survival ) . Continuous data take a range of values ( for example , change in diastolic blood pressure after antihypertensive treatment , measured in mm Hg ) . When one is comparing groups of patients , binary data can be summarized by using several measures of treatment effect that were discussed earlier in this series [ 3 ] . These measures include the risk ratio ; the odds ratio ; the risk difference ; and , when study duration is important , the incidence rate . Another useful clinical measure , the number needed to treat ( NNT ) , is derived from the inverse of the risk difference [ 3 ] . Treatment effect measures , such as the risk ratio and the odds ratio , provide an estimate of the relative efficacy of an intervention , whereas the risk difference describes the intervention 's absolute benefit . The various measures of treatment effect offer complementary information , and all should be examined [ 4 ] . Continuous data can be summarized by the raw mean difference between the treatment and control groups when the treatment effect is measured on the same scale ( for example , diastolic blood pressure in mm Hg ) , by the st and ardized mean difference when different scales are used to measure the same treatment effect ( for example , different pain scales being combined ) , or by the correlation coefficients between two continuous variables [ 5 ] . The st and ardized mean difference , also called the effect size , is obtained by dividing the difference between the mean in the treatment group and the mean in the control group by the SD in the control group . Evaluating the Statistical Heterogeneity of the Data This step is intended to answer the question , Are the results of the different studies similar ( homogeneous ) ? It is important to answer this question before combining any data . To do this , one must calculate the magnitude of the statistical diversity ( heterogeneity ) of the treatment effect that exists among the different sets of data . Statistical diversity can be thought of as attributable to one or both of two causes . First , study results can differ because of r and om sampling error . Even if the true effect is the same in each study , the results of different studies would be expected to vary r and omly around the true common fixed effect . This diversity is called the within- study variance . Second , each study may have been drawn from a different population , depending on the particular patients chosen and the interventions and conditions unique to the study . Therefore , even if each study enrolled a large patient sample , the treatment effect would be expected to differ . These differences , called r and om effects , describe the between- study variation with regard to an overall mean of the effects of all of the studies that could be undertaken . The test most commonly used to assess the statistical significance of between- study heterogeneity is based on the chi-square distribution [ 6 ] . It provides a measure of the sum of the squared differences between the results observed and the results expected in each study , under the assumption that each study estimates the same common treatment effect . A large total deviation indicates that a single common treatment effect is unlikely . Any pooled estimate calculated must account for the between- study heterogeneity . In practice , this test has low sensitivity for detecting heterogeneity , and it has been suggested that a liberal significance level , such as 0.1 , should be used [ 6 ] . Estimating a Common Effect The questions that this step tries to answers are , 1 ) To the extent that data are similar , what is their best common point estimate of a therapeutic effect , and 2 ) how precise is this estimate ? The mathematical process involved in this step generally involves combining ( pooling ) the results of different studies into an overall estimate . Compared with the results of individual studies , pooled results can increase statistical power and lead to more precise estimates of treatment effect . Each study is given a weight according to the precision of its results . The rationale is that studies with narrow CIs should be weighted more heavily than studies with greater uncertainty . The precision is generally expressed by the inverse of the variance of the estimate of each study . The variance has two components : the variance of the individual study and the variance between different studies . When the between- study variance is found to be or assumed to be zero , each study is simply weighted by the inverse of its own variance , which is a function of the study size and the number of events in the study . This approach characterizes a fixed-effects model , as exemplified by the Mantel-Haenszel method [ 7 , 8 ] or the Peto method [ 9 ] for dichotomous data . The Peto method has been particularly popular in the past . It has the advantage of simple calculation ; however , although it is appropriate in most cases , it may introduce large biases if the data are unbalanced [ 10 , 11 ] . On the other h and , r and om-effects models also add the between- study variance to the within- study variance of each individual study when the pooled mean of the r and om effects is calculated . The r and om-effects model most commonly used for dichotomous data is the DerSimonian and Laird estimate of the between- study variance [ 12 ] . Fixed- and r and om- 
    Output:
    
This study suggests that VEGF expression is significantly correlated with the glioma progression and may be a valuable prognostic factor on OS for the glioma patients