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Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature
In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic fact...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5071205/ https://www.ncbi.nlm.nih.gov/pubmed/27785429 http://dx.doi.org/10.21037/tau.2016.09.07 |
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author | Mbeutcha, Aurélie Mathieu, Romain Rouprêt, Morgan Gust, Kilian M. Briganti, Alberto Karakiewicz, Pierre I. Shariat, Shahrokh F. |
author_facet | Mbeutcha, Aurélie Mathieu, Romain Rouprêt, Morgan Gust, Kilian M. Briganti, Alberto Karakiewicz, Pierre I. Shariat, Shahrokh F. |
author_sort | Mbeutcha, Aurélie |
collection | PubMed |
description | In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease. |
format | Online Article Text |
id | pubmed-5071205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-50712052016-10-26 Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature Mbeutcha, Aurélie Mathieu, Romain Rouprêt, Morgan Gust, Kilian M. Briganti, Alberto Karakiewicz, Pierre I. Shariat, Shahrokh F. Transl Androl Urol Review Article In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease. AME Publishing Company 2016-10 /pmc/articles/PMC5071205/ /pubmed/27785429 http://dx.doi.org/10.21037/tau.2016.09.07 Text en 2016 Translational Andrology and Urology. All rights reserved. |
spellingShingle | Review Article Mbeutcha, Aurélie Mathieu, Romain Rouprêt, Morgan Gust, Kilian M. Briganti, Alberto Karakiewicz, Pierre I. Shariat, Shahrokh F. Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature |
title | Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature |
title_full | Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature |
title_fullStr | Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature |
title_full_unstemmed | Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature |
title_short | Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature |
title_sort | predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5071205/ https://www.ncbi.nlm.nih.gov/pubmed/27785429 http://dx.doi.org/10.21037/tau.2016.09.07 |
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