OBJECTIVE: To examine the ability of preoperative clinical characteristics to predict histological features of renal masses (RMs). @entity1 AND METHODS: Data from consecutive @entity1 with clinical stage I RMs treated surgically between 2010 and 2011 in the Clinical Research Office of Endourology Society (CROES) Renal Mass Registry were collected. Based on surgical histology, @entity5 were categorised as benign, low- or high- @entity146 @entity5 . Multivariate logistic regression was used to estimate the probability of the histological group by clinical and radiographic features in the entire cohort and a subcohort of cT1a @entity5 . The performance of the models was studied by calibration, Nagelkerke's R(2) , and discrimination (area under the receiver operating characteristic curve). RESULTS: The study cohort included 2 224 @entity1 with a clinical stage I RM, of which 1 367 (61%) were cT1a. Benign lesions were found in 369 (16.6%), @entity5 in 1 156 (52%) and @entity5 in 699 (31.4%). Male gender, smoking history, increased @entity5 size, and lower exophytic rate were associated with @entity5 and high- @entity146 features (all P < 0.05). Models developed based on these characteristics had the ability to discriminate benign from malignant (bootstrap corrected c-index of 0.64) and @entity5 from benign and @entity5 (bootstrap corrected c-index of 0.66). Similar results were achieved in the cT1a subgroup. The c-index of @entity5 diameter as a single predictor of @entity5 in the entire cohort was 0.6 and 0.63, respectively. CONCLUSION: Although older age, male gender, smoking history, increased @entity5 diameter, and reduced exophytic rate are associated with @entity5 and high @entity146 of clinical stage I RMs, models incorporating these characteristics have modest discriminating power, being only slightly better than the predictive ability of @entity5 size alone.

Prediction of renal mass XXXX using clinical and radiographic features: a global, multicentre prospective study.

@entity1 :: ('9606', 'Species') :: ['patients', 'PATIENTS'] @entity5 :: ('MESH:D009369', 'Disease') :: ['low-aggressiveness tumours', 'cancer', 'high-aggressiveness tumours', 'tumours', 'malignancy and high-aggressiveness tumours', 'malignancy', 'tumour'] @entity146 :: ('MESH:D001523', 'Disease') :: ['aggressiveness']
@entity146 :: (MESH:D001523,Disease) :: ['aggressiveness']
