We developed postoperative nomograms, integrating competing risks, and conditional probabilities for prediction of disease recurrence and cancer-specific mortality in chemotherapy-naive pT1-3N0 patients treated with RC. Nomograms currently represent the most accurate and discriminatory tools to predict probabilities of outcomes after RC (Shariat et al, 2008d
). The clinical utility (i.e., clinical decision-making regarding adjuvant chemotherapy) of previous published RC nomograms has been limited by their inclusion of all tumour stages (i.e., pT0-4, any N) of uncommon and/or extraneous histologies, and of patients receiving peri-operative chemotherapy (Bochner et al, 2006
; Karakiewicz et al, 2006a
; Shariat et al, 2006a
). To overcome these limitations, we developed nomograms that specifically address outcomes of patients with pT1-3N0 UCB, who did not receive peri-operative chemotherapy. As a significant proportion of pT1-3N0 patients are likely to die from non-cancer-related causes, we used competing-risk analyses (Cheng et al, 1998
) to estimate the probability of cancer-specific mortality with higher accuracy. Our nomograms further incorporated conditional probabilities in order to allow accurate patient counselling not only in the immediate postoperative setting but also during the various stages of follow-up. A patient's probability of a future event changes over time; for example, a patient's risk of disease recurrence within 5 years after RC is higher the day after surgery than if the patient has no recurrence at 2 years. In UCB, as a patients' prognosis is expected to improve with increasing disease-free interval, absence of adjustment for this variable results in an excessively somber estimate of cancer control over time. Therefore, we provide nomograms that adjust for the effect of disease-free interval following surgery. As expected, the predicted risk of disease recurrence decreases with increasing disease-free interval.
We found that the prognosis of RC patients with pT1-3N0 UCB can be predicted with reasonable accuracy (ranging from 64–69%). Although this rate could be considered moderate, it is within the range of performance of commonly used tools in the management of patients with prostate cancer (Shariat et al, 2008b
). Moreover, because of the highly heterogeneous outcomes of this specific patient group, it is very difficult for a clinician to prognosticate recurrence and survival. Currently, for example, only pT3N0 patients are considered for adjuvant chemotherapy. Although T-stage represents the strongest single predictor of outcomes in pT1-3N0 patients, addition of readily available pathological variables such as LVI and STSM improved the accuracy of our models by a statistically and prognostically significant margin (Shariat et al, 2006b
). Nevertheless, it is evident that even the best combination of standard clinico-pathological features is insufficient to achieve optimal prediction in pT1-3N0 UCB. Blood- and tissue-based biomarkers may represent a ‘fast, easy, cheap, and powerful' method to enhance the accuracy of the current multivariable prognostic/predictive tools. We and others have shown that integration of biomarkers improves the prediction of outcomes in pT1-3N0 UCB patients by a statistically and clinically substantial margin (Gakis et al, 2011
; Shariat et al, 2012
). There is no doubt that panels of biomarkers that capture the biological and clinical behaviour of each individual tumour will be necessary to serve as prognosticators, predictors, therapeutic targets, and/or surrogate end points in order to usher the much awaited personalised oncology. We internally validated the performance of our nomograms in the US population it was built on, and externally in an European population (Rink et al, 2012
). Differences in disease and population characteristics may undermine the discrimination and calibration of predictive tools when applied to a different population. Specific criteria used in defining the sample used to develop a prediction tool may not allow the use of tools for patients with different characteristics or who have been exposed to different treatment strategies. Indeed, there were several significant differences between the US and European pT1-3N0 patient cohorts (i.e., different rates of T3, high-grade, CIS, LVI, STSM). Therefore, external validation in different contemporary cohorts is necessary to ensure generalisability of prediction tools (Shariat et al, 2008b
Published studies have added to general knowledge of the best candidate for adjuvant chemotherapy after RC, but physicians and patients have few tools to help them translate this body of knowledge into individualised, evidence-based recommendations. Although the power from the published randomised adjuvant chemotherapy trials remains limited (Advanced Bladder Cancer (ABC) Meta-analysis Collaboration, 2005
; von der Maase et al, 2005
), patients with lymph node involvement or metastatic disease are usually counselled in favour of adjuvant chemotherapy, as it has been suggested to improve disease-free survival in this population (Advanced Bladder Cancer (ABC) Meta-analysis Collaboration, 2005
; von der Maase et al, 2005
; Stenzl et al, 2011
). In pT1-3N0 patients, the data is largely underpowered and insufficient to allow evidence-based clinical decision-making. Accurate prediction of outcomes in these patients may alleviate some of the quandary of practitioners and patients alike when faced with the potentially beneficial but toxic adjuvant therapy. Equipped with accurate prediction and a personalised clinical decision, patients are more likely to be confident in their treatment decisions and less likely to experience regret in the future (Shariat et al, 2008b
). Multivariable nomograms such as the one we propose currently represent the most accurate and widely used prediction tool in oncology (Shariat et al, 2008b
). Until better tools are available, our nomograms based on pT-stage, LVI, and STSM could help in the risk stratification of neoadjuvant chemotherapy-naive pT1-3N0 UCB patients for adjuvant chemotherapy after RC. Using decision analysis, Vickers et al (2009)
recently demonstrated that nomograms can improve clinical decision-making regarding referral of patients with bladder cancer for adjuvant chemotherapy after RC. They found that nomograms outperformed current decision-making strategies based on T- and N-stage to determine which pT1-4 any N bladder cancer patient would benefit from adjuvant chemotherapy, taking into account drug effectiveness and tolerability. For example, using the nomogram to identify patients with a 25% risk of disease recurrence after surgery alone as an indication for adjuvant chemotherapy reduced unnecessary treatment by approximately 25% compared with the standard approach of using pathological stage criteria.
The current study suffers from several limitations. First and foremost are the limitations inherent to the retrospective multicentre study design. The population in this study underwent RC by multiple surgeons and had specimens evaluated by multiple pathologists. However, all surgeons operated at selected tertiary care centres with significant experience in UCB management, which might increase the external validity of the data compared with the single-centre, single-surgeon setting. In addition, we did not perform a centralised pathological review, which could have led to misinterpretations of pathological specimens and underreporting. However, whereas it may be preferable for a single pathologist specialising in genitourinary pathology to review each RC specimen, the present study reflects the real-world practice. Moreover, dedicated genitourinary pathologists examined all specimens. Finally, it may be argued that the patients in our database would currently be candidates for neoadjuvant chemotherapy, thereby questioning the current applicability of our data, which were generated from patients who did not receive peri-operative systemic chemotherapy. Despite the evidence regarding the efficacy of neoadjuvant chemotherapy, to date, only 9–22% of muscle-invasive UCB patients receive neoadjuvant chemotherapy before RC (Burger et al, 2012
Despite radical surgery with curative intent, a significant number of patients with pT1-3N0 UCB will experience disease recurrence and, ultimately, death. We developed competing-risk, conditional probability nomograms that predict the outcomes of chemotherapy-naive pT1-3N0 UCB patients with reasonable accuracy. We internally validated the nomograms in a US population and externally validated them in a European population. Such nomograms may improve the clinical decision-making process regarding adjuvant chemotherapy and may assist in inclusion for clinical trials.