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Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules
OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57–73) years; 37% females) with pulmonary nodules on non-enhanced thin-se...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181968/ https://www.ncbi.nlm.nih.gov/pubmed/36538071 http://dx.doi.org/10.1007/s00330-022-09334-w |
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author | Peters, Alan A. Weinheimer, Oliver von Stackelberg, Oyunbileg Kroschke, Jonas Piskorski, Lars Debic, Manuel Schlamp, Kai Welzel, Linn Pohl, Moritz Christe, Andreas Ebner, Lukas Kauczor, Hans-Ulrich Heußel, Claus Peter Wielpütz, Mark O. |
author_facet | Peters, Alan A. Weinheimer, Oliver von Stackelberg, Oyunbileg Kroschke, Jonas Piskorski, Lars Debic, Manuel Schlamp, Kai Welzel, Linn Pohl, Moritz Christe, Andreas Ebner, Lukas Kauczor, Hans-Ulrich Heußel, Claus Peter Wielpütz, Mark O. |
author_sort | Peters, Alan A. |
collection | PubMed |
description | OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57–73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS: Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (−766 vs. −790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62−0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS: Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS: • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules. |
format | Online Article Text |
id | pubmed-10181968 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-101819682023-05-14 Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules Peters, Alan A. Weinheimer, Oliver von Stackelberg, Oyunbileg Kroschke, Jonas Piskorski, Lars Debic, Manuel Schlamp, Kai Welzel, Linn Pohl, Moritz Christe, Andreas Ebner, Lukas Kauczor, Hans-Ulrich Heußel, Claus Peter Wielpütz, Mark O. Eur Radiol Chest OBJECTIVES: To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation. METHODS: A total of 251 subjects (median [IQR] age, 65 (57–73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model. RESULTS: Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (−766 vs. −790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62−0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001). CONCLUSIONS: Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules. KEY POINTS: • Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant. • The nodule-bearing lobes have less emphysema compared to the rest of the lung. • QCT variables could improve the risk assessment of incidental pulmonary nodules. Springer Berlin Heidelberg 2022-12-20 2023 /pmc/articles/PMC10181968/ /pubmed/36538071 http://dx.doi.org/10.1007/s00330-022-09334-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Chest Peters, Alan A. Weinheimer, Oliver von Stackelberg, Oyunbileg Kroschke, Jonas Piskorski, Lars Debic, Manuel Schlamp, Kai Welzel, Linn Pohl, Moritz Christe, Andreas Ebner, Lukas Kauczor, Hans-Ulrich Heußel, Claus Peter Wielpütz, Mark O. Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules |
title | Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules |
title_full | Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules |
title_fullStr | Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules |
title_full_unstemmed | Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules |
title_short | Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules |
title_sort | quantitative ct analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules |
topic | Chest |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181968/ https://www.ncbi.nlm.nih.gov/pubmed/36538071 http://dx.doi.org/10.1007/s00330-022-09334-w |
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