Platinum Priority – Collaborative Review – Bladder CancerEditorial by Jo Cresswell and A. Hugh Mostafid on pp. 254–255 of this issuePrognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature☆
Introduction
Bladder cancer (BCa) is a heterogeneous disease with high prevalence and recurrence rates [1], [2], [3]. Prognostication and risk assessment are essential for treatment decision making, patient counseling, and inclusion in clinical trials. Outcome prediction based on a physician's experience alone may be subjectively influenced [4], [5]. Most commonly, decisions that involve prediction are based on risk categories. Cancer stage represents the simplest and most commonly used example of a prediction tool. The American Joint Committee on Cancer (AJCC) TNM staging system has been validated and is used widely to predict the risk of disease recurrence in patients treated with radical cystectomy (RC) [6]. These staging systems provide useful estimates of survival outcome, with more aggressive treatments reserved for patients with higher stage disease. However, current staging systems have been shown to be less accurate at prediction than prediction models that incorporate several clinical variables; furthermore, staging systems cannot easily incorporate novel information such as molecular markers or more complex bioinformatics. In addition to standard oncologic features, patients with BCa are generally elderly and have significant comorbidities, resulting in the need for competing-risk analyses to be able to choose individualized therapies.
All of this complex information requires tools that can integrate multiple disparate data points for each individual patient to allow personalized medicine. In recent years, a plethora of published papers have described different prediction tools relying on common pre- and postoperative clinical and pathologic parameters in BCa. Their goal is to facilitate and improve daily clinical practice though integration of evidence-based information or data [7], [8], [9]. However, the vast majority of clinical decisions for BCa are still made without reference to prediction tools or any type of decision aid [10]. A possible explanation for the low adoption rate of prediction tools is that they have not been demonstrated to improve clinical decision making for specific clinical decisions on external validation. Another explanation is that prediction tools have rarely been integrated into electronic medical records so that they are available to the doctor at the point of care.
In patients with non–muscle-invasive bladder cancer (NMIBC), prediction tools could have an impact on the decision-making process regarding surveillance schedules and administration of intravesical therapy (immediate postoperative instillation of chemotherapy (IPIC) and/or adjuvant chemotherapy [11]. Accurate preoperative prediction tools could help predict risks of progression, enabling better selection of clinical T1 high-grade patients who should undergo RC as primary treatment (early RC) compared with intravesical bacillus Calmette-Guérin (BCG). In patients with high-risk NMIBC and muscle-invasive bladder cancer (MIBC) undergoing RC, accurate prediction of the presence of lymph node (LN) metastasis and the probability of disease recurrence could provide guidance for selecting patients who have an imperative need for perioperative systemic chemotherapy integrated with extended LN dissection at the time of RC [12]. These scenarios are examples of common crossroads or dilemmas in the daily management of patients with BCa.
We have analyzed and identified the best clinical tools and scenarios for use with prognostic and prediction models in managing BCa and proposed a pathway for their validation and integration into clinical practice.
Section snippets
Evidence acquisition
A literature search of the English literature was performed using PubMed in July 2013 using the keywords bladder cancer, radical cystectomy, prediction, predictive tool, nomogram, risk grouping, risk table, decision curve, decision tool, and prognosis. Before circulating the last draft, we performed a final literature search in March 2014 to add any meaningful references. More than 1600 publications were retrieved. Relevant papers were preselected by two authors (L.A.K. and S.F.S.), and all
Currently available prediction tools
We provide an overview of the currently available prediction tools for BCa. We present the prediction tools by specific clinical problems or questions in NMIBC, MIBC, and metastatic BCa, summarizing predictor variables, the number of patients used for development, tool-specific features, discrimination, calibration, and whether internal and/or external validation was performed (Table 1).
Prediction of disease recurrence and progression in patients with non–muscle-invasive bladder cancer [7,13–24]
In a large study cohort of 1529 patients with NMIBC, Millan-Rodriguez et al examined predictors of disease
Conclusions
Prognostic and prediction tools with high discriminative accuracy may help facilitate the decision-making process and potentially improve clinical outcomes for BCa patients, but these tools need external validation to show good calibration and improve outcome on indices that are of direct relevance for making clinical decisions, such as the PPV, and especially decision analysis (net benefit). Few tools have met these criteria. Integration of complex data such as genomics and epigenetics are
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