***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: PURPOSE Development of a radiosensitivity predictive assay is a central goal of radiation oncology . We reasoned a gene expression model could be developed to predict intrinsic radiosensitivity and treatment response in patients . METHODS AND MATERIAL S Radiosensitivity ( determined by survival fraction at 2 Gy ) was modeled as a function of gene expression , tissue of origin , ras status ( mut/wt ) , and p53 status ( mut/wt ) in 48 human cancer cell lines . Ten genes were identified and used to build a rank-based linear regression algorithm to predict an intrinsic radiosensitivity index ( RSI , high index = radioresistance ) . This model was applied to three independent cohorts treated with concurrent chemoradiation : head- and -neck cancer ( HNC , n = 92 ) ; rectal cancer ( n = 14 ) ; and esophageal cancer ( n = 12 ) . RESULTS Predicted RSI was significantly different in responders ( R ) vs. nonresponders ( NR ) in the rectal ( RSI R vs. NR 0.32 vs. 0.46 , p = 0.03 ) , esophageal ( RSI R vs. NR 0.37 vs. 0.50 , p = 0.05 ) and combined rectal/esophageal ( RSI R vs. NR 0.34 vs. 0.48 , p = 0.001511 ) cohorts . Using a threshold RSI of 0.46 , the model has a sensitivity of 80 % , specificity of 82 % , and positive predictive value of 86 % . Finally , we evaluated the model as a prognostic marker in HNC . There was an improved 2-year locoregional control ( LRC ) in the predicted radiosensitive group ( 2-year LRC 86 % vs. 61 % , p = 0.05 ) . CONCLUSIONS We vali date a robust multigene expression model of intrinsic tumor radiosensitivity in three independent cohorts totaling 118 patients . To our knowledge , this is the first time that a systems biology-based radiosensitivity model is vali date d in multiple independent clinical data sets 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 BACKGROUND Despite its common use in cancer treatment , radiotherapy has not yet entered the era of precision medicine , and there have been no approaches to adjust dose based on biological differences between or within tumours . We aim ed to assess whether a patient-specific molecular signature of radiation sensitivity could be used to identify the optimum radiotherapy dose . METHODS We used the gene-expression-based radiation-sensitivity index and the linear quadratic model to derive the  genomic-adjusted radiation dose ( GARD ) . A high GARD value predicts for high therapeutic effect for radiotherapy ; which we postulate would relate to clinical outcome . Using data from the prospect i ve , observational Total Cancer Care ( TCC ) protocol , we calculated GARD for primary tumours from 20 disease sites treated using st and ard radiotherapy doses for each disease type . We also used multivariable Cox modelling to assess whether GARD was independently associated with clinical outcome in five clinical cohorts : Erasmus Breast Cancer Cohort ( n=263 ) ; Karolinska Breast Cancer Cohort ( n=77 ) ; Moffitt Lung Cancer Cohort ( n=60 ) ; Moffitt Pancreas Cancer Cohort ( n=40 ) ; and The Cancer Genome Atlas Glioblastoma Patient Cohort ( n=98 ) . FINDINGS We calculated GARD for 8271 tissue sample s from the TCC cohort . There was a wide range of GARD values ( range 1·66 - 172·4 ) across the TCC cohort despite assignment of uniform radiotherapy doses within disease types . Median GARD values were lowest for gliomas and sarcomas and highest for cervical cancer and oropharyngeal head and neck cancer . There was a wide range of GARD values within tumour type groups . GARD independently predicted clinical outcome in breast cancer , lung cancer , glioblastoma , and pancreatic cancer . In the Erasmus Breast Cancer Cohort , 5-year distant-metastasis-free survival was longer in patients with high GARD values than in those with low GARD values ( hazard ratio 2·11 , 95 % 1·13 - 3·94 , p=0·018 ) . INTERPRETATION A GARD-based clinical model could allow the individualisation of radiotherapy dose to tumour radiosensitivity and could provide a framework to design genomically-guided clinical trials in radiation oncology . FUNDING None OBJECTIVE : To test the feasibility of creating a valid and reliable checklist with the following features : appropriate for assessing both r and omised and non-r and omised studies ; provision of both an overall score for study quality and a profile of scores not only for the quality of reporting , internal validity ( bias and confounding ) and power , but also for external validity . DESIGN : A pilot version was first developed , based on epidemiological principles , review s , and existing checklists for r and omised studies . Face and content validity were assessed by three experienced review ers and reliability was determined using two raters assessing 10 r and omised and 10 non-r and omised studies . Using different raters , the checklist was revised and tested for internal consistency ( Kuder-Richardson 20 ) , test-retest and inter-rater reliability ( Spearman correlation coefficient and sign rank test ; kappa statistics ) , criterion validity , and respondent burden . MAIN RESULTS : The performance of the checklist improved considerably after revision of a pilot version . The Quality Index had high internal consistency ( KR-20 : 0.89 ) as did the subscales apart from external validity ( KR-20 : 0.54 ) . Test-retest ( r 0.88 ) and inter-rater ( r 0.75 ) reliability of the Quality Index were good . Reliability of the subscales varied from good ( bias ) to poor ( external validity ) . The Quality Index correlated highly with an existing , established instrument for assessing r and omised studies ( r 0.90 ) . There was little difference between its performance with non-r and omised and with r and omised studies . Raters took about 20 minutes to assess each paper ( range 10 to 45 minutes ) . CONCLUSIONS : This study has shown that it is feasible to develop a checklist that can be used to assess the method ological quality not only of r and omised controlled trials but also non-r and omised studies . It has also shown that it is possible to produce a checklist that provides a profile of the paper , alerting review ers to its particular method ological strengths and weaknesses . Further work is required to improve the checklist and the training of raters in the assessment of external validity 
    Output:
    
Conclusions These findings may have implication s for patient and treatment selection , dose fractionation , and support the hypothesis that CRC pulmonary metastases may require higher biological effective doses while respecting normal tissue constraints when treated with SBRT