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Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography

BACKGROUND: Necrotic pulmonary lesions manifest as relatively low-density internally on contrast-enhanced computed tomography (CT). However, using CT to differentiate malignant and benign necrotic pulmonary lesions is challenging, as these lesions have similar peripheral enhancement. With the introd...

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Autores principales: Li, Qi, Fan, Xiao, Luo, Tian-You, Lv, Fa-Jin, Huang, Xing-Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117597/
https://www.ncbi.nlm.nih.gov/pubmed/33985454
http://dx.doi.org/10.1186/s12880-021-00611-6
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author Li, Qi
Fan, Xiao
Luo, Tian-You
Lv, Fa-Jin
Huang, Xing-Tao
author_facet Li, Qi
Fan, Xiao
Luo, Tian-You
Lv, Fa-Jin
Huang, Xing-Tao
author_sort Li, Qi
collection PubMed
description BACKGROUND: Necrotic pulmonary lesions manifest as relatively low-density internally on contrast-enhanced computed tomography (CT). However, using CT to differentiate malignant and benign necrotic pulmonary lesions is challenging, as these lesions have similar peripheral enhancement. With the introduction of dual-energy spectral CT (DESCT), more quantitative parameters can be obtained and the ability to differentiate material compositions has been highly promoted. This study investigated the use of kVp-switching DESCT in differentiating malignant from benign necrotic lung lesions. METHODS: From October 2016 to February 2019, 40 patients with necrotic lung cancer (NLC) and 31 with necrotic pulmonary mass-like inflammatory lesion (NPMIL) were enrolled and underwent DESCT. The clinical characteristics of patients, CT morphological features, and DESCT quantitative parameters of lesions were compared between the two groups. Binary logistic regression analysis was performed to identify the independent prognostic factors differentiating NPMIL from NLC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analyses. RESULTS: Significant differences in age, C-reactive protein concentration, the slope of the spectral curve from 40 to 65 keV (K(40–65 keV)) of necrosis in non-contrast-enhanced scanning (NCS), arterial phase (AP) and venous phase (VP), effective atomic number of necrosis in NCS, and iodine concentration (IC) of the solid component in VP were observed between groups (all p < 0.05). The aforementioned parameters had area under the ROC curve (AUC) values of 0.747, 0.691, 0.841, 0.641, 0.660, 0.828, and 0.754, respectively, for distinguishing between NLC and NPMIL. Multiparametric analysis showed that age, K(40–65 keV) of necrosis in NCS, and IC of the solid component in VP were the most effective factors for differentiating NLC from NPMIL, with an AUC of 0.966 and percentage of correct class of 88.7%. CONCLUSIONS: DESCT can differentiate malignant from benign necrotic lung lesions with a relatively high accuracy.
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spelling pubmed-81175972021-05-13 Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography Li, Qi Fan, Xiao Luo, Tian-You Lv, Fa-Jin Huang, Xing-Tao BMC Med Imaging Research BACKGROUND: Necrotic pulmonary lesions manifest as relatively low-density internally on contrast-enhanced computed tomography (CT). However, using CT to differentiate malignant and benign necrotic pulmonary lesions is challenging, as these lesions have similar peripheral enhancement. With the introduction of dual-energy spectral CT (DESCT), more quantitative parameters can be obtained and the ability to differentiate material compositions has been highly promoted. This study investigated the use of kVp-switching DESCT in differentiating malignant from benign necrotic lung lesions. METHODS: From October 2016 to February 2019, 40 patients with necrotic lung cancer (NLC) and 31 with necrotic pulmonary mass-like inflammatory lesion (NPMIL) were enrolled and underwent DESCT. The clinical characteristics of patients, CT morphological features, and DESCT quantitative parameters of lesions were compared between the two groups. Binary logistic regression analysis was performed to identify the independent prognostic factors differentiating NPMIL from NLC. Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of single-parameter and multiparametric analyses. RESULTS: Significant differences in age, C-reactive protein concentration, the slope of the spectral curve from 40 to 65 keV (K(40–65 keV)) of necrosis in non-contrast-enhanced scanning (NCS), arterial phase (AP) and venous phase (VP), effective atomic number of necrosis in NCS, and iodine concentration (IC) of the solid component in VP were observed between groups (all p < 0.05). The aforementioned parameters had area under the ROC curve (AUC) values of 0.747, 0.691, 0.841, 0.641, 0.660, 0.828, and 0.754, respectively, for distinguishing between NLC and NPMIL. Multiparametric analysis showed that age, K(40–65 keV) of necrosis in NCS, and IC of the solid component in VP were the most effective factors for differentiating NLC from NPMIL, with an AUC of 0.966 and percentage of correct class of 88.7%. CONCLUSIONS: DESCT can differentiate malignant from benign necrotic lung lesions with a relatively high accuracy. BioMed Central 2021-05-13 /pmc/articles/PMC8117597/ /pubmed/33985454 http://dx.doi.org/10.1186/s12880-021-00611-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Li, Qi
Fan, Xiao
Luo, Tian-You
Lv, Fa-Jin
Huang, Xing-Tao
Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography
title Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography
title_full Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography
title_fullStr Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography
title_full_unstemmed Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography
title_short Differentiating malignant and benign necrotic lung lesions using kVp-switching dual-energy spectral computed tomography
title_sort differentiating malignant and benign necrotic lung lesions using kvp-switching dual-energy spectral computed tomography
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8117597/
https://www.ncbi.nlm.nih.gov/pubmed/33985454
http://dx.doi.org/10.1186/s12880-021-00611-6
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