***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: Introduction : Accurate prognosis assessment after non – small-cell lung cancer ( NSCLC ) diagnosis is an essential step for making effective clinical decisions . This study is aim ed to develop a prediction model with routinely available variables to assess prognosis in patients with NSCLC in the U.S. Military Health System . Methods : We used the linked data base from the Department of Defense ’s Central Cancer Registry and the Military Health System Data Repository . The data set was r and omly and equally split into a training set to guide model development and a testing set to vali date the model prediction . Stepwise Cox regression was used to identify predictors of survival . Model performance was assessed by calculating area under the receiver operating curves and construction of calibration plots . A simple risk scoring system was developed to aid quick risk score calculation and risk estimation for NSCLC clinical management . Results : The study subjects were 5054 patients diagnosed with NSCLC between 1998 and 2007 . Age , sex , tobacco use , tumor stage , histology , surgery , chemotherapy , peripheral vascular disease , cerebrovascular disease , and diabetes mellitus were identified as significant predictors of survival . Calibration showed high agreement between predicted and observed event rates . The area under the receiver operating curves reached 0.841 , 0.849 , 0.848 , and 0.838 during 1 , 2 , 3 , and 5 years , respectively . Conclusions : This is the first NSCLC prognosis model for quick risk assessment within the Military Health System . After external validation , the model can be translated into clinical use both as a web-based tool and through mobile applications easily accessible to physicians , patients , and research ers 7556 Background : Prognostic index for second-line chemotherapy of NSCLC was previously developed , based on individual patient data ( IPD ) of 9 r and omized trials ( Table ) ( Di Maio et al , Eur J Cancer 2010 ; 46 : 735 - 743 ) . In this study , we aim ed to vali date the prognostic score , with the use of an external data set . METHODS We analyzed IPD of patients ( pts ) enrolled in a non-inferiority phase III trial comparing vinflunine vs. docetaxel in second-line treatment of advanced NSCLC . The study showed similar efficacy for both agents . Primary endpoint of this analysis was overall survival ( OS ) . The following variables were considered for survival analysis and score calculation : gender , performance status ( PS ) , extent of disease , tumor histotype , type of first-line treatment , response to first-line treatment . Cox model , stratified by treatment arm , was used for multivariate analysis . Individual prognostic scores were derived according to our scoring system , and pts were divided into 3 categories according to the score : < 5 ( best ) , 5 - 9 ( intermediate ) , > 9 ( worst ) . RESULTS All 551 pts enrolled in the trial had complete information for prognostic score calculation . Median OS in all pts was 6.9 months . Median OS was 12.9 , 6.9 and 3.8 months for the best , intermediate and worst categories , respectively . Cox model analysis showed a significant effect for intermediate vs. best category comparison ( hazard ratio 1.79 , 95%CI 1.31 - 2.47 , p=0.0003 ) and for worst vs. best category comparison ( hazard ratio 3.25 , 95%CI 2.18 - 4.83 , p<0.0001 ) . The C-index of the model was high ( 0.926 ) , indicating a good discrimination according to the proposed risk categories . CONCLUSIONS Prognostic ability of our score for c and i date s to second-line treatment in advanced NSCLC was vali date d. Subgroups of patients with more vs. less favorable prognosis were identified . Prognostic score could be useful in clinical practice . [ Table : see text ] Objectives Previous studies have shown that Glasgow prognostic score ( GPS ) and prognostic index ( PI ) are also powerful prognostic tool for patients with advanced non-small cell lung cancer ( NSCLC ) . The aim of this study was to compare the prognostic value between GPS and PI . Methods We enrolled consecutive patients with advanced NSCLC in this prospect i ve cohort .  GPS and PI were calculated before the onset of chemotherapy . The prognosis outcomes included 1- , 3- , and 5-year progression-free survival and overall survival ( OS ) . The performance of two scores in predicting prognosis was analyzed regarding discrimination and calibration . Results 138 patients were included in the study . The area under the receiver operating characteristic curve for GPS  predicting 1-year DFS was 0.62 ( 95 % confidence interval ( CI ) 0.56–0.68 , P < 0.05 ) , and the area under curve for PI predicting 1-year DFS was 0.57 ( 95 % CI 0.52–0.63 ) . Delong ’s test showed that GPS was more accurate than PI in predicting 1-year DFS ( P < 0.05 ) . Similar results of discriminatory power were found for predicting 3-year DFS , 1-year OS , and 3-year OS . The predicted 1-year DFS by GPS 0 , GPS 1 , and GPS 2 were 62.5 , 42.1 , and 23.1 % , respectively , while actual 1-year DFS by GPS 0 , GPS 1 , and GPS 2 were 61.1 , 43.8 , and 27.2 % , respectively . Calibration of the Hosmer and Lemeshow statistic showed good fit of the predicted 1-year DFS to the actual 1-year DFS by GPS ( χ2 = 4.326 , P = 0.462 ) , while no fit was found between the predicted 1-year DFS and the actual 1-year DFS by PI ( χ2 = 15.234 , P = 0.091 ) . Similar results of calibration power were found for predicting 3-year DFS , 5-year DFS , 1-year OS , 3-year OS , and 5-year OS  by GPS and PI . Conclusions GPS is more accurate than PI in predicting prognosis for patients with advanced NSCLC . GPS can be used as a useful and simple tool for predicting prognosis in patients with NSCLC . However , GPS only can be used for preliminary assessment because of low predicting accuracy Early identification and treatment of nutritional deficiencies can lead to improved outcomes in the quality of life ( QoL ) and survival of patients with nonsmall cell lung cancer ( NSCLC ) . Noninvasive techniques are needed to evaluate changes in body composition as part of determining nutritional status . The aim of the study was to evaluate the association of nutritional parameters in health-related quality of life ( HRQL ) and survival in patients with advanced NSCLC . Chemotherapy-naïve patients with advanced NSCLC with good performance status Eastern Cooperative Oncology Group ( ECOG ) 0–2 were included prospect ively in the study . We evaluated inflammatory parameters such as C-reactive protein , platelet/lymphocyte index , neutrophil/lymphocyte index , serum interleukin (IL)-6 , and tumor necrosis factor-α , and nutritional variables such as body mass index ( BMI ) and serum albumin levels . Bioelectrical impedance analysis including phase angle was obtained before cisplatin-based chemotherapy was started  . HRQL was assessed by application of the European Organization for Research and Treatment of Cancer Quality of Life Question naire (QLQ)-C30 and QLQ-LC13 instruments at baseline . Overall survival ( OS ) was calculated with the Kaplan-Meier method and analyzed with log-rank and Cox proportional hazard models . One hundred nineteen patients were included .  Mean BMI was 24.8 ± 4.5 kg/m2 , average weight loss of patients was 8.4 % , and median phase angle was 5.8 ° . Malnutrition measured by subjective global assessment ( SGA ) , weight loss > 10 % , BMI > 20 was associated with lower HRQL scales . Patients with ECOG 2 , high content serum IL-6 , lower phase angle , and malnutrition parameters showed lower OS ; however , after multivariate analysis , only ECOG 2 [ Hazard ratio ( HR ) , 2.7 ; 95 % confidence interval ( 95 % CI ) , 1.5–4.7 ; P = 0.001 ] , phase angle ≤5.8 ° ( HR = 3.02 ; 95 % CI : 1.2–7.11 ; P = 0.011 ) , and SGA ( HR = 2.7 ; 95 % CI , 1.31–5.5 ; P = 0.005 ) were associated with poor survival . Patients were divided into low- , intermediate- , and high-risk groups according to regression coefficients ; OS at 1 yr was 78.4 , 53 , and 13.8 % , respectively . Malnutrition is associated with low HRQL and is an independent prognostic factor in advanced NSCLC . The results warrant prospect i ve trials to evaluate the impact of different nutritional interventions on HRQL and survival Recent advances in the treatment of non-small cell lung cancer ( NSCLC ) have led to improvements in patient survival and quality of life . It is unclear whether molecular abnormalities associated with NSCLC cell survival , growth and proliferation are useful in predicting treatment benefit . We conducted a systematic review to establish which biomarkers contribute meaningfully to the management of NSCLC . A team of research ers search ed PubMed and conference proceedings ( ASCO , ESMO , IASLC , USCAP ) using MESH terms for NSCLC and r and omized trials ( RCT ) , plus keywords for variables of interest . Evidence from multiple RCTs confirmed that histologic subtype is prognostic for survival and predictive of treatment efficacy and /or toxicity in NSCLC . Likewise , activating mutations of the epidermal growth factor receptor ( EGFR ) are associated with benefit from EGFR tyrosine kinase inhibitors in patients with advanced non-squamous NSCLC and should be assessed routinely . No biomarkers to date reliably predict response to anti-Vascular Endothelial Growth Factor ( VEGF ) therapies . There are inconsistent data on the role of ERCC1 , BRCA , Beta tubulin III , RRM1 , K-RAS , or TP-53 in treatment decisions . These tests should not be routinely used in selecting treatment at this time , whereas EML4/ALK translocations predict responses to specific targeted agents , the optimal assessment of this molecular abnormality has yet to be established . Personalized care of patients with NSCLC based on biomarkers is increasingly important to both clinical practice and research Introduction : BR.21 demonstrated significant survival benefit for non-small cell lung cancer patients receiving erlotinib compared with placebo .  We undertook to characterize , by exploratory subset analysis , patients less likely to benefit from erlotinib .  Methods : Using stratification and potential prognostic factors , Cox regression with stepwise selection with minimum Akaike Information Criteria was used to separate erlotinib patients into risk categories based on 10th , 50th , and 90th percentiles of prognostic index scores . The hypothesis was that characteristics of treated patients in the highest risk group would be predictive of lack of benefit from erlotinib when comparing erlotinib to placebo patients in the same risk group . Results : Ten factors ( smoking history , performance status , weight loss , anemia , lactic dehydrogenase , response to prior chemotherapy , time from diagnosis , number of prior regimens , epidermal growth factor receptor copy , and ethnicity ) were predictive of overall survival for erlotinib-treated patients and were used in the final model . Four risk groups were derived from the index score of the Prognostic Model : Low Risk ( HR = 0.34 , p < 0.001 ) , Intermediate Low and Intermediate High Risk ( HR 0.76 , p = 0.05 ; HR 0.92 ; p = 0.51 ) and High Risk ( HR 1.07 ; p = 0.78 ) . Median survivals for erlotinib ( placebo ) patients in each group were 20.6 ( 8.9 ) , 10.4 ( 7.6 ) , 4.0 ( 4.1 ) , 1.9 ( 2.3 ) months . The trend test showed that higher risk was associated with shorter survival ( p < 0.001 ) and less treatment effect ( p = 0.03 ) . Conclusions : By establishing a prognostic model , we identified a small group of patients who did not seem to benefit from erlotinib in this study . This model requires prospect i ve validation to confirm that it is both prognostic and predictive of outcome BACKGROUND Lung cancer is the commonest cause of death due to cancer in the world . Non-small-cell lung carcinoma ( NSCLC ) represents about 80 % of overall lung cancer cases worldwide . An accurate predictive model of mortality in patients with NSCLC could be useful to clinicians , policy makers , and research ers involved in risk stratification . The objective of this study was to develop and vali date a simple prognostic index for 4-year mortality in patients with NSCLC by use of information obtained at the time of lung cancer diagnosis . METHODS In 2000 , 4669 patients with histologically or cytologically proven NSCLC were enrolled prospect ively from 137 pneumology departments in French general hospitals . Patients not lost to follow-up ( n=4479 ) were r and omly assigned to the development cohort ( n=2979 ) or the validation cohort ( n=1500 ) . Every patient 's physician completed a st and ard and anonymous question naire . We used a Cox model to identify variables independently associated with mort 
    Output:
    
The 39 identified DSS aim to predict overall survival and /or progression-free survival , but give no information about toxicity or cost-effectiveness .
Conclusions Many DSS have been developed for incurable NSCLC patients , but DSS are still lacking that are up-to- date with a good model performance , while covering the entire treatment spectrum .