Immunocytology helps predict bladder cancer
MedWire News: Immunocytology is a strong predictor of urothelial carcinoma of the bladder (UCB) in patients with painless hematuria, show study results.
Furthermore, adding immunocytology to prediction models or nomograms for UCB improves diagnostic accuracy in a statistically and clinically significant way, report the study authors.
"We developed highly accurate, well-calibrated nomograms to help in the clinical decision-making process regarding patient counseling, referral prioritization, and possibly the extent of diagnostic workup for patients with painless hematuria," say Shahrokh Shariat (Weill Cornell Medical College, New York, USA) and colleagues.
Although cystourethroscopy, which forms part of the standard workup of patients with painless hematuria, is sensitive and specific at predicting UCB, it is invasive and costly, they observe.
Immunocytology is already known to be more sensitive than urinary cytology in surveillence of existing UCB patients; Shariat and team investigated its use in the detection setting.
The study cohort comprised 919 men and 263 women, aged a median 65 years, of whom 68% presented with microscopic hematuria, and 32% presented with gross hematuria. After urine samples were examined cytologically and immunocytologically, 245 (20.7%) of the participants were diagnosed with UCB.
Shariat et al report sensitivity and specificity, and positive and negative predictive values of 46.5%, 94.9%, 70.4%, and 87.2% for cytology for predicting UCB, respectively. The corresponding values for immunocytology were all higher, at 82.4%, 86.6%, 61.6%, and 95.0%.
Multivariate analysis revealed that in addition to the known variables associated with UCB in this type of cohort (age, smoking history, and gross hematuria), immunocytology and cytology each showed a significant independent association (odds ratios [ORs]=18.26 and 2.92).
In receiver operating characteristic analysis, the area under the curve score (AUC) for a prediction model incorporating the known variables (plus gender) was 73.8%, where 100% denotes perfect discrimination of UCB.
This score improved significantly when cytology was added to the model, giving an AUC of 83.1%, while adding immunocytology improved the AUC to a high of 90.4% (also significant).
Of note, the addition of immunocytology to the prediction model that included cytology improved the AUC by a significant 7.7%. However, adding cytology to a model involving immunocytology did not improve it (0.4%).
The researchers also undertook decision curve analysis, which showed that prediction models containing immunocytology showed significantly higher net benefit than models with cytology, up to threshold probabilities of 60%.
Indeed, with a policy of giving cystourethroscopies to all patients with threshold probabilities of UCB of at least 2% on the full predictive models (including cytology and immunocytology), 211 fewer cystourethroscopies would be performed and only one cancer would be missed, calculate Shariat and co-workers.
"Prediction tools such as nomograms have been shown to perform better than clinical judgement," they say.
"That being said, physician input is obviously essential in medical decision-making, both for the measurement of predictive variables and for the interpretation and application of prediction tools in clinical practice," the team concludes.
By Sarah Guy