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Nomograms in Urologic Oncology: Lights and Shadows
Decision-making in urologic oncology involves integrating multiple clinical data to provide an answer to the needs of a single patient. Although the practice of medicine has always been an “art” involving experience, clinical data, scientific evidence and judgment, the creation of specialties and su...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7957873/ https://www.ncbi.nlm.nih.gov/pubmed/33801184 http://dx.doi.org/10.3390/jcm10050980 |
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author | Morlacco, Alessandro Modonutti, Daniele Motterle, Giovanni Martino, Francesca Dal Moro, Fabrizio Novara, Giacomo |
author_facet | Morlacco, Alessandro Modonutti, Daniele Motterle, Giovanni Martino, Francesca Dal Moro, Fabrizio Novara, Giacomo |
author_sort | Morlacco, Alessandro |
collection | PubMed |
description | Decision-making in urologic oncology involves integrating multiple clinical data to provide an answer to the needs of a single patient. Although the practice of medicine has always been an “art” involving experience, clinical data, scientific evidence and judgment, the creation of specialties and subspecialties has multiplied the challenges faced every day by physicians. In the last decades, with the field of urologic oncology becoming more and more complex, there has been a rise in tools capable of compounding several pieces of information and supporting clinical judgment and experience when approaching a difficult decision. The vast majority of these tools provide a risk of a certain event based on various information integrated in a mathematical model. Specifically, most decision-making tools in the field of urologic focus on the preoperative or postoperative phase and provide a prognostic or predictive risk assessment based on the available clinical and pathological data. More recently, imaging and genomic features started to be incorporated in these models in order to improve their accuracy. Genomic classifiers, look-up tables, regression trees, risk-stratification tools and nomograms are all examples of this effort. Nomograms are by far the most frequently used in clinical practice, but are also among the most controversial of these tools. This critical, narrative review will focus on the use, diffusion and limitations of nomograms in the field of urologic oncology. |
format | Online Article Text |
id | pubmed-7957873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-79578732021-03-16 Nomograms in Urologic Oncology: Lights and Shadows Morlacco, Alessandro Modonutti, Daniele Motterle, Giovanni Martino, Francesca Dal Moro, Fabrizio Novara, Giacomo J Clin Med Review Decision-making in urologic oncology involves integrating multiple clinical data to provide an answer to the needs of a single patient. Although the practice of medicine has always been an “art” involving experience, clinical data, scientific evidence and judgment, the creation of specialties and subspecialties has multiplied the challenges faced every day by physicians. In the last decades, with the field of urologic oncology becoming more and more complex, there has been a rise in tools capable of compounding several pieces of information and supporting clinical judgment and experience when approaching a difficult decision. The vast majority of these tools provide a risk of a certain event based on various information integrated in a mathematical model. Specifically, most decision-making tools in the field of urologic focus on the preoperative or postoperative phase and provide a prognostic or predictive risk assessment based on the available clinical and pathological data. More recently, imaging and genomic features started to be incorporated in these models in order to improve their accuracy. Genomic classifiers, look-up tables, regression trees, risk-stratification tools and nomograms are all examples of this effort. Nomograms are by far the most frequently used in clinical practice, but are also among the most controversial of these tools. This critical, narrative review will focus on the use, diffusion and limitations of nomograms in the field of urologic oncology. MDPI 2021-03-02 /pmc/articles/PMC7957873/ /pubmed/33801184 http://dx.doi.org/10.3390/jcm10050980 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Morlacco, Alessandro Modonutti, Daniele Motterle, Giovanni Martino, Francesca Dal Moro, Fabrizio Novara, Giacomo Nomograms in Urologic Oncology: Lights and Shadows |
title | Nomograms in Urologic Oncology: Lights and Shadows |
title_full | Nomograms in Urologic Oncology: Lights and Shadows |
title_fullStr | Nomograms in Urologic Oncology: Lights and Shadows |
title_full_unstemmed | Nomograms in Urologic Oncology: Lights and Shadows |
title_short | Nomograms in Urologic Oncology: Lights and Shadows |
title_sort | nomograms in urologic oncology: lights and shadows |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7957873/ https://www.ncbi.nlm.nih.gov/pubmed/33801184 http://dx.doi.org/10.3390/jcm10050980 |
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