Immunocytology is a strong predictor of bladder cancer presence in patients with painless hematuria: a multicentre study

Abstract

Background: 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.

Objective: 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.

Intervention: All patients underwent upper-tract imaging, cystourethroscopy, voided urine cytology, and immunocytology analysis. Bladder tumors were biopsied and histologically confirmed as UCB.

Measurements: 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.

Conclusions: 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.

Fig. 1

Nomogram for the prediction of bladder cancer (BCa) presence in patients with painless hematuria, where age, gender, smoking history, degree of hematuria, urine cytology, and immunocytology define the risk of BCa at cystourethroscopy. Nomogram instructions: To obtain the nomogram-predicted probability of BCa at cystourethroscopy, locate patient values on each axis. Draw a vertical line to the Points axis to determine how many points are attributed for each variable value. Sum the points for all variables. Locate the sum on the Total Points line to assess the individual probability of BCa at cystourethroscopy on the Probability of BCa line. BCa = bladder cancer.

Fig. 2

Calibration plot, where the x-axis represents the predicted probability and the y-axis represents the observed fraction of bladder cancer in 1182 patients with painless hematuria. The 45° dashed line represents ideal predictions, the solid line (bias-corrected) represents the internally validated predictions (using 200 bootstrap samples), and the dotted line (apparent) represents the uncorrected predictions. The scatter plot at the top of the figure shows the distribution of the individual nomogram-predicted probabilities.

Fig. 3

(a) Decision curve analysis of the effect of prediction models for detection of urothelial carcinoma of the bladder (UCB) in 1182 patients with painless hematuria. Net benefit is plotted against threshold probabilities. (b) Expanded view of decision curves in the range of threshold probabilities from 1% to 10%. (c) Net benefit and reduction in avoidable cystourethroscopies for each model compared with the “cystourethroscopy all” strategy. Example of derivation of net benefit from prediction models in Figure 3c: At a 5% threshold, the value of 0.1809 (“net benefit from prediction model” for base model plus cytology plus immunocytology) is derived from the following 2 × 2 table. This table classifies patients with a predicted probability from the multivariable model ≥5% as positive for UCB and a predicted probability < 5% as negative for UCB (ie, the chosen threshold value). This predicted probability classification is then compared with the true UCB status for the patient. [Table: see text] Net benefit = true positive proportion − [false positive proportion × (0.05/0.95)] = 235/1182 − [403/1182 × 0.0526] = 0.1809 The reduction in the number of unnecessary cystourethroscopies per 100 patients is then calculated as follows: (net benefit of the model − net benefit of treat all)/(pt/(1 − pt)) × 100, where pt is the threshold probability. This value is net of false negatives and is therefore the equivalent of the reduction in unnecessary cystourethroscopies without a decrease in the number of patients with UCB who duly have cystourethroscopy. Example of net reduction in avoidable cystourethroscopies in Fig. 3c: For base model + cytology + immunocytology at 5% threshold probability, we would get: (0.1809 − 0.1656)/(0.05/0.95) × 100 = 29.1% (29.1 avoidable cystourethroscopies per 100 patients).