Accurate prediction is essential for patient counselling, appropriate selection of treatments and determination of eligibility for clinical trials. In this review we assess the available determinants of oncological outcome after radical cystectomy (RC) for transitional cell carcinoma of the urinary bladder. We reviewed previous publications to provide guidelines in terms of criteria, limitations and clinical value of available tools for predicting patient outcome after RC. Our findings suggest that while individual surgical, patient and pathological features provide useful estimates of survival outcome, the inherent heterogeneity of tumour biology and patient characteristics leads to significant variation in outcome. By incorporating all relevant continuous predictive factors for individual patients, integrative predictive models, such as nomograms or artificial neural networks, provide more accurate predictions and generally surpass clinical experts at predicting outcomes. Nonetheless, there is a clear need for the development and validation of molecular biomarkers and their incorporation into multivariable predictive tools. Significant progress has been made in identifying important molecular markers of disease and the development of multifactorial tools for predicting the outcome after RC.

Predicting survival after radical cystectomy for bladder cancer

MONTORSI , FRANCESCO;
2008

Abstract

Accurate prediction is essential for patient counselling, appropriate selection of treatments and determination of eligibility for clinical trials. In this review we assess the available determinants of oncological outcome after radical cystectomy (RC) for transitional cell carcinoma of the urinary bladder. We reviewed previous publications to provide guidelines in terms of criteria, limitations and clinical value of available tools for predicting patient outcome after RC. Our findings suggest that while individual surgical, patient and pathological features provide useful estimates of survival outcome, the inherent heterogeneity of tumour biology and patient characteristics leads to significant variation in outcome. By incorporating all relevant continuous predictive factors for individual patients, integrative predictive models, such as nomograms or artificial neural networks, provide more accurate predictions and generally surpass clinical experts at predicting outcomes. Nonetheless, there is a clear need for the development and validation of molecular biomarkers and their incorporation into multivariable predictive tools. Significant progress has been made in identifying important molecular markers of disease and the development of multifactorial tools for predicting the outcome after RC.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11768/2657
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