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Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma
Preoperative identification of unresectable pleural mesothelioma could spare unnecessary surgical intervention and accelerate the initiation of medical treatments. The aim of this study is to determine predictors of unresectability, testing our impression that the contraction of the ipsilateral hemi...
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/PMC8509574/ https://www.ncbi.nlm.nih.gov/pubmed/34640425 http://dx.doi.org/10.3390/jcm10194407 |
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author | Bellini, Alice Dell’Amore, Andrea Giraudo, Chiara Modugno, Antonella Bernardinello, Nicol Terzi, Stefano Zambello, Giovanni Pasello, Giulia Zuin, Andrea Rea, Federico |
author_facet | Bellini, Alice Dell’Amore, Andrea Giraudo, Chiara Modugno, Antonella Bernardinello, Nicol Terzi, Stefano Zambello, Giovanni Pasello, Giulia Zuin, Andrea Rea, Federico |
author_sort | Bellini, Alice |
collection | PubMed |
description | Preoperative identification of unresectable pleural mesothelioma could spare unnecessary surgical intervention and accelerate the initiation of medical treatments. The aim of this study is to determine predictors of unresectability, testing our impression that the contraction of the ipsilateral hemithorax is often associated with exploratory thoracotomy. Between 1994 and 2020, 291 patients undergoing intended macroscopic complete resection for mesothelioma after chemotherapy were retrospectively investigated. Eligible patients (n = 58) presented a preoperative 3 mm slice-thickness chest computed tomography without pleural effusion or hydropneumothorax. Lung volumes (segmented using a semi-automated method), modified-Response Evaluation Criteria in Solid Tumors (RECIST) measurements, and spirometries were collected after chemotherapy. Multivariable analysis was performed to determine the predictors of unresectability. An unresectable disease was found at the time of operation in 25.9% cases. By multivariable analysis, the total lung capacity (p = 0.03) and the disease burden (p = 0.02) were found to be predictors of unresectability; cut-off values were <77.5% and >120.5 mm, respectively. Lung volumes were not confirmed to be associated with unresectability at multivariable analysis, probably due to the correlation with the disease burden (p < 0.001; r = −0.4). Our study suggests that disease burden and total lung capacity could predict MPM unresectability, helping surgeons in recommending surgery or not in a multimodality setting. |
format | Online Article Text |
id | pubmed-8509574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85095742021-10-13 Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma Bellini, Alice Dell’Amore, Andrea Giraudo, Chiara Modugno, Antonella Bernardinello, Nicol Terzi, Stefano Zambello, Giovanni Pasello, Giulia Zuin, Andrea Rea, Federico J Clin Med Article Preoperative identification of unresectable pleural mesothelioma could spare unnecessary surgical intervention and accelerate the initiation of medical treatments. The aim of this study is to determine predictors of unresectability, testing our impression that the contraction of the ipsilateral hemithorax is often associated with exploratory thoracotomy. Between 1994 and 2020, 291 patients undergoing intended macroscopic complete resection for mesothelioma after chemotherapy were retrospectively investigated. Eligible patients (n = 58) presented a preoperative 3 mm slice-thickness chest computed tomography without pleural effusion or hydropneumothorax. Lung volumes (segmented using a semi-automated method), modified-Response Evaluation Criteria in Solid Tumors (RECIST) measurements, and spirometries were collected after chemotherapy. Multivariable analysis was performed to determine the predictors of unresectability. An unresectable disease was found at the time of operation in 25.9% cases. By multivariable analysis, the total lung capacity (p = 0.03) and the disease burden (p = 0.02) were found to be predictors of unresectability; cut-off values were <77.5% and >120.5 mm, respectively. Lung volumes were not confirmed to be associated with unresectability at multivariable analysis, probably due to the correlation with the disease burden (p < 0.001; r = −0.4). Our study suggests that disease burden and total lung capacity could predict MPM unresectability, helping surgeons in recommending surgery or not in a multimodality setting. MDPI 2021-09-26 /pmc/articles/PMC8509574/ /pubmed/34640425 http://dx.doi.org/10.3390/jcm10194407 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Bellini, Alice Dell’Amore, Andrea Giraudo, Chiara Modugno, Antonella Bernardinello, Nicol Terzi, Stefano Zambello, Giovanni Pasello, Giulia Zuin, Andrea Rea, Federico Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma |
title | Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma |
title_full | Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma |
title_fullStr | Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma |
title_full_unstemmed | Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma |
title_short | Computed Tomography and Spirometry Can Predict Unresectability in Malignant Pleural Mesothelioma |
title_sort | computed tomography and spirometry can predict unresectability in malignant pleural mesothelioma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8509574/ https://www.ncbi.nlm.nih.gov/pubmed/34640425 http://dx.doi.org/10.3390/jcm10194407 |
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