Although the performance of immunocytology has been established in the surveillance of patients with urothelial carcinoma of the bladder (UCB), its value in the initial detection of UCB in patients with painless hematuria remains unclear.
To determine whether immunocytology improves our ability to predict the likelihood of UCB in patients with painless hematuria. Further, to test the clinical benefit of immunocytology in this setting using decision curve analysis.
Design, setting, and participants
The subjects were 1182 consecutive patients without a history of UCB presenting with painless hematuria and were enrolled at three centres.
All patients underwent upper-tract imaging, cystourethroscopy, voided urine cytology, and immunocytology analysis. Bladder tumors were biopsied and histologically confirmed as UCB.
Multivariable regression models were developed. Area under the curve was measured and compared using the DeLong test. A nomogram was constructed from the full multivariable model. Decision curve analysis was performed to evaluate the clinical benefit associated with use of the multivariable models including immunocytology.
Results and limitations
Immunocytology had the largest contribution to a multivariable model for the prediction of UCB (odds ratio: 18.3; p < 0.0001), which achieved a 90.8% predictive accuracy. Decision curve analysis revealed that models incorporating immunocytology achieved the highest net benefit at all threshold probabilities.
Immunocytology is a strong predictor of the presence of UCB in patients who present with painless hematuria. Incorporation of immunocytology into predictive models improves diagnostic accuracy by a statistically and clinically significant margin. The use of immunocytology in the diagnostic workup of patients with hematuria appears promising and should be further evaluated.