***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 The results of R and omized Controlled Trials ( RCTs ) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio . These do not constitute the sufficient statistics required for meta- analysis or cost-effectiveness analysis , and their use in secondary analyses requires strong assumptions that may not have been adequately tested . In order to enhance the quality of secondary data analyses , we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated . Methods We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations , using where available information on number of events and numbers at risk . The reproducibility and accuracy of survival probabilities , median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics ( survival probabilities , medians and hazard ratios ) with statistics based on repeated reconstructions by multiple observers . Results The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics . Accuracy was excellent for survival probabilities and medians , for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported . Conclusion The algorithm is a reliable tool for meta- analysis and cost-effectiveness analyses of RCTs reporting time-to-event data . It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves PURPOSE Model-based treatment-plan-specific outcome predictions ( such as normal tissue complication probability [ NTCP ] or the relative reduction in salivary function ) are typically presented without reference to underlying uncertainties . We provide a method to assess the reliability of treatment-plan-specific dose-volume outcome model predictions . METHODS AND MATERIAL S A practical method is proposed for evaluating model prediction based on the original input data together with bootstrap-based estimates of parameter uncertainties . The general framework is applicable to continuous variable predictions ( e.g. , prediction of long-term salivary function ) and dichotomous variable predictions ( e.g. , tumor control probability [ TCP ] or NTCP ) . Using bootstrap resampling , a histogram of the likelihood of alternative parameter values is generated . For a given patient and treatment plan we generate a histogram of alternative model results by computing the model predicted outcome for each parameter set in the bootstrap list . Residual uncertainty ( " noise " ) is accounted for by adding a r and om component to the computed outcome values . The residual noise distribution is estimated from the original fit between model predictions and patient data . RESULTS The method is demonstrated using a continuous-endpoint model to predict long-term salivary function for head- and -neck cancer patients . Histograms represent the probabilities for the level of posttreatment salivary function based on the input clinical data , the salivary function model , and the three-dimensional dose distribution . For some patients there is significant uncertainty in the prediction of xerostomia , whereas for other patients the predictions are expected to be more reliable . In contrast , TCP and NTCP endpoints are dichotomous , and parameter uncertainties should be folded directly into the estimated probabilities , thereby improving the accuracy of the estimates . Using bootstrap parameter estimates , competing treatment plans can be ranked based on the probability that one plan is superior to another . Thus , reliability of plan ranking could also be assessed . CONCLUSIONS A comprehensive framework for incorporating uncertainties into treatment-plan-specific outcome predictions is described . Uncertainty histograms for continuous variable endpoint models provide a straightforward method for visual review of the reliability of outcome predictions for each treatment plan Primary and metastatic tumors of the central nervous system are a heterogeneous group of neoplasms with varied outcomes and management strategies . Recently , improved survival observed in 2 r and omized clinical trials established combined chemotherapy and radiation as the new st and ard for treating patients with pure or mixed anaplastic oligodendroglioma harboring the 1p/19q codeletion . For metastatic disease , increasing evidence supports the efficacy of stereotactic radiosurgery in treating patients with multiple metastatic lesions but low overall tumor volume . These guidelines provide recommendations on the diagnosis and management of this group of diseases based on clinical evidence and panel consensus . This version includes expert advice on the management of low- grade infiltrative astrocytomas , oligodendrogliomas , anaplastic gliomas , glioblastomas , medulloblastomas , supratentorial primitive neuroectodermal tumors , and brain metastases . The full online version , available at NCCN . org , contains recommendations on additional subtypes 
    Output:
    
FSRT provides high rates of LC for patients with brain metastases .
Advances in knowledge : This review identified a dose-response relationship for LC in patients treated with FSRT for brain metastases