OBJECTIVES: The objective of the study was to assess whether pretreatment clinical parameters combined with computed tomography can improve the prediction of lymph node metastasis in patients with bladder cancer treated with radical cystectomy.
PATIENTS AND METHODS: In a single-center retrospective study, demographic and clinicopathological information (initial transurethral resection (grade, stage, multiplicity of tumors, lymphovascular invasion), hydronephrosis, abdominal and pelvic computed tomography) and the presence of lymph node disease on final pathology of 183 patients with bladder cancer undergoing radical cystectomy and pelvic lymph node dissection were reviewed. Logistic regression and bootstrap methods were used to create an integer score for estimating the risk of positive lymph nodes. Various measures for predictive ability and clinical utility were determined.
RESULTS: On pathological examination, 59.6 % of patients had positive lymph nodes. In a multivariable analysis, status lymph nodes on computed tomography and hydronephrosis were the most strongly associated predictors. The resultant total possible score ranged from 0 to 10, with a cut-off value of >4 points. The area under the receiver operating characteristic curve was 0.806. Relative integrated discrimination improvement was 14.3 %. In the decision curve analysis, the model provided net benefit throughout the entire range of threshold probabilities. However, the final model was roughly equivalent to using the clinical exam.
CONCLUSIONS: The pre-cystectomy scoring system improved the prediction of lymph node status in patients with bladder cancer. Our model represented a user-friendly staging aid, but a large multi-center study should be performed before widespread implementation.
Written by:
Stojadinović MM, Prelević R, Vukićević A. Are you the author?
Department of Urology, Clinic of Urology and Nephrology, Clinical Centre "Kragujevac", Zmaj Jovina 30, 34000, Kragujevac, Serbia.
Reference: Int Urol Nephrol. 2014 Jan 29. Epub ahead of print.
doi: 10.1007/s11255-014-0645-x
PubMed Abstract
PMID: 24474218
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