Luis A Kluth1, Peter C Black2, Bernard H Bochner3, James Catto4, Seth P Lerner5, Arnulf Stenzl6, Richard Sylvester7, Andrew J Vickers8, Evanguelos Xylinas9, Shahrokh F Shariat10. 1. Department of Urology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA; Department of Urology, University Medical-Center Hamburg-Eppendorf, Hamburg, Germany. 2. Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada. 3. Department of Urology, Memorial Sloan-Kettering Cancer Center, Kimmel Center for Prostate and Urologic Tumors, New York, NY, USA. 4. Academic Urology Unit, University of Sheffield, Sheffield, UK. 5. Scott Department of Urology, Baylor College of Medicine, Houston, TX, USA. 6. Department of Urology, Eberhard-Karls University, Tuebingen, Germany. 7. EORTC Headquarters, Brussels, Belgium. 8. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA. 9. Department of Urology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA; Department of Urology, Cochin Hospital, Assistance Publique-Hôpitaux de Paris, Paris Descartes University, Paris, France. 10. Department of Urology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA; Department of Urology, Medical University of Vienna, Vienna, Austria; Department of Urology, UT Southwestern, Dallas, TX, USA; Division of Medical Oncology, Weill Cornell Medical College, New York Presbyterian Hospital, New York, NY, USA. Electronic address: sfshariat@gmail.com.
Abstract
CONTEXT: This review focuses on risk assessment and prediction tools for bladder cancer (BCa). OBJECTIVE: To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa. EVIDENCE ACQUISITION: A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool. EVIDENCE SYNTHESIS: Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non-muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa. CONCLUSIONS: Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care. PATIENT SUMMARY: We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve clinical decision making.
CONTEXT: This review focuses on risk assessment and prediction tools for bladder cancer (BCa). OBJECTIVE: To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa. EVIDENCE ACQUISITION: A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool. EVIDENCE SYNTHESIS: Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non-muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa. CONCLUSIONS: Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care. PATIENT SUMMARY: We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve clinical decision making.
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