BACKGROUND: We sought to determine which parsimonious combination of @entity1503 ( @entity1503 )-based biomarkers most efficiently predicts oncologic outcomes in @entity1 undergoing radical cystectomy (RC) for @entity150 ( @entity150 ). METHODS: Using our institutional RC database (1992-2012), nine @entity1503 -based markers (including both absolute cell counts and ratios) were evaluated based on pre-treatment measurements. The outcome measures were @entity1349 ( @entity1349 ), @entity5 -specific survival (CSS), and @entity1349 ( @entity1349 ). Time-dependent receiver-operating characteristics curves were used to characterise each biomarker. The @entity1503 -based biomarkers, along with several clinical predictors, were then considered for inclusion in predictive multivariable Cox models based on the Akaike Information Criterion. RESULTS: Our cohort included 418 @entity1 . Neutrophil-lymphocyte ratio (NLR) was the only biomarker satisfying criteria for inclusion into all models, independently predicting @entity1349 (HR per 1-log unit=1.52, 95% CI=1.17-1.98, P=0.002), CSS (HR=1.47, 95% CI=1.20-1.80, P<0.001), and @entity1349 (HR=1.56, 95% CI=1.16-2.10, P=0.004). Haemoglobin was also independently predictive of CSS (HR per 1   g/dl=0.91, 95% CI=0.86-0.95, P<0.001) and @entity1349 (HR=0.90, 95% CI=0.88-0.93, P<0.001), but not @entity1349 . CONCLUSIONS: Among @entity1503 biomarkers studied, NLR was the most efficient marker for predicting @entity1349 , whereas NLR and haemoglobin were most efficient in predicting CSS and @entity1349 . NLR and haemoglobin are promising, cost-effective, independent biomarkers for predicting oncologic @entity150 outcomes following RC. CONDENSED ABSTRACT: Various @entity1503 -based biomarkers have separately been shown to be predictive of oncologic outcomes in @entity1 undergoing cystectomy for @entity150 . Our study evaluated these biomarkers, and determined that NLR is the best @entity1503 -based biomarker for predicting @entity1349 , whereas NLR and haemoglobin are most efficient for predicting CSS and @entity1349 .

Identification of the best @entity1503 -based predictors for @entity150 outcomes in XXXX undergoing radical cystectomy.

@entity1 :: ('9606', 'Species') :: ['patients'] @entity5 :: ('MESH:D009369', 'Disease') :: ['cancer'] @entity1503 :: ('MESH:D006402', 'Disease') :: ['CBC', 'complete blood count'] @entity150 :: ('MESH:D001749', 'Disease') :: ['BC', 'bladder cancer'] @entity1349 :: ('MESH:D011475', 'Disease') :: ['OS', 'RFS', 'recurrence-free survival', 'overall survival']
@entity1 :: (9606,Species) :: ['patients']
