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Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules?

Persistent pure ground-glass nodules (pGGNs) typically show an indolent course with very slow growth rates. These slow-growing lesions exhibit different growth patterns regardless of their initial computed tomography (CT) features. Therefore, predicting the aggressive behavior of pGGNs on initial CT...

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Detalles Bibliográficos
Autores principales: Borghesi, Andrea, Michelini, Silvia, Bertagna, Francesco, Scrimieri, Alessandra, Pezzotti, Stefania, Maroldi, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170928/
https://www.ncbi.nlm.nih.gov/pubmed/30294620
http://dx.doi.org/10.1016/j.ejro.2018.09.004
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author Borghesi, Andrea
Michelini, Silvia
Bertagna, Francesco
Scrimieri, Alessandra
Pezzotti, Stefania
Maroldi, Roberto
author_facet Borghesi, Andrea
Michelini, Silvia
Bertagna, Francesco
Scrimieri, Alessandra
Pezzotti, Stefania
Maroldi, Roberto
author_sort Borghesi, Andrea
collection PubMed
description Persistent pure ground-glass nodules (pGGNs) typically show an indolent course with very slow growth rates. These slow-growing lesions exhibit different growth patterns regardless of their initial computed tomography (CT) features. Therefore, predicting the aggressive behavior of pGGNs on initial CT remains a diagnostic challenge. The literature reports that computerized analysis and various quantitative features have been tested to improve the risk stratification for pGGNs. The present article describes the long-term follow-up of two pGGNs with different behavior and introduces, for the first time, a new computerized method of analysis that could be helpful for predicting the future behavior of pGGNs.
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spelling pubmed-61709282018-10-05 Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules? Borghesi, Andrea Michelini, Silvia Bertagna, Francesco Scrimieri, Alessandra Pezzotti, Stefania Maroldi, Roberto Eur J Radiol Open Article Persistent pure ground-glass nodules (pGGNs) typically show an indolent course with very slow growth rates. These slow-growing lesions exhibit different growth patterns regardless of their initial computed tomography (CT) features. Therefore, predicting the aggressive behavior of pGGNs on initial CT remains a diagnostic challenge. The literature reports that computerized analysis and various quantitative features have been tested to improve the risk stratification for pGGNs. The present article describes the long-term follow-up of two pGGNs with different behavior and introduces, for the first time, a new computerized method of analysis that could be helpful for predicting the future behavior of pGGNs. Elsevier 2018-10-02 /pmc/articles/PMC6170928/ /pubmed/30294620 http://dx.doi.org/10.1016/j.ejro.2018.09.004 Text en © 2018 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Borghesi, Andrea
Michelini, Silvia
Bertagna, Francesco
Scrimieri, Alessandra
Pezzotti, Stefania
Maroldi, Roberto
Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules?
title Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules?
title_full Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules?
title_fullStr Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules?
title_full_unstemmed Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules?
title_short Hilly or mountainous surface: a new CT feature to predict the behavior of pure ground glass nodules?
title_sort hilly or mountainous surface: a new ct feature to predict the behavior of pure ground glass nodules?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6170928/
https://www.ncbi.nlm.nih.gov/pubmed/30294620
http://dx.doi.org/10.1016/j.ejro.2018.09.004
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