Cargando…

A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules

BACKGROUND: Lung cancer is the most common cancer which has the highest mortality rate. With the development of computed tomography (CT) techniques, the case detection rates of solitary pulmonary nodules (SPN) has constantly increased and the diagnosis accuracy of SPN has remained a hot topic in cli...

Descripción completa

Detalles Bibliográficos
Autores principales: Xiao, Huijuan, Liu, Yihe, Tan, Hongna, Liang, Pan, Wang, Bo, Su, Lei, Wang, Suya, Gao, Jianbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4647278/
https://www.ncbi.nlm.nih.gov/pubmed/26576676
http://dx.doi.org/10.1186/s12880-015-0096-6
_version_ 1782401065647341568
author Xiao, Huijuan
Liu, Yihe
Tan, Hongna
Liang, Pan
Wang, Bo
Su, Lei
Wang, Suya
Gao, Jianbo
author_facet Xiao, Huijuan
Liu, Yihe
Tan, Hongna
Liang, Pan
Wang, Bo
Su, Lei
Wang, Suya
Gao, Jianbo
author_sort Xiao, Huijuan
collection PubMed
description BACKGROUND: Lung cancer is the most common cancer which has the highest mortality rate. With the development of computed tomography (CT) techniques, the case detection rates of solitary pulmonary nodules (SPN) has constantly increased and the diagnosis accuracy of SPN has remained a hot topic in clinical and imaging diagnosis. The aim of this study was to evaluate the combination of low-dose spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm in the diagnosis of solitary pulmonary nodules (SPN). METHODS: 62 patients with SPN (42 cases of benign SPN and 20 cases of malignant SPN, pathology confirmed) were scanned by spectral CT with a dual-phase contrast-enhanced method. The iodine and water concentration (IC and WC) of the lesion and the artery in the image that had the same density were measured by the GSI (Gemstone Spectral Imaging) software. The normalized iodine and water concentration (NIC and NWC) of the lesion and the normalized iodine and water concentration difference (ICD and WCD) between the arterial and venous phases (AP and VP) were also calculated. The spectral HU (Hounsfield Unit ) curve was divided into 3 sections based on the energy (40–70, 70–100 and 100–140 keV) and the slopes (λHU) in both phases were calculated. The IC(AP), IC(VP), WC(AP) and WC(VP), NIC and NWC, and the λHU in benign and malignant SPN were compared by independent sample t-test. RESULTS: The iodine related parameters (IC(AP), IC(VP), NIC(AP), NIC(VP), and the ICD) of malignant SPN were significantly higher than that of benign SPN (t = 3.310, 1.330, 2.388, 1.669 and 3.251, respectively, P <0.05). The 3 λHU values of venous phase in malignant SPN were higher than that of benign SPN (t = 3.803, 2.846 and 3.205, P <0.05). The difference of water related parameters (WC(AP), WC(VP), NWC(AP), NWC(VP) and WCD) between malignant and benign SPN were not significant (t = 0.666, 0.257, 0.104, 0.550 and 0.585, P >0.05). CONCLUSIONS: The iodine related parameters and the slope of spectral curve are useful markers to distinguish the benign from the malignant lung diseases, and its application is extremely feasible in clinical applications.
format Online
Article
Text
id pubmed-4647278
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-46472782015-11-18 A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules Xiao, Huijuan Liu, Yihe Tan, Hongna Liang, Pan Wang, Bo Su, Lei Wang, Suya Gao, Jianbo BMC Med Imaging Research Article BACKGROUND: Lung cancer is the most common cancer which has the highest mortality rate. With the development of computed tomography (CT) techniques, the case detection rates of solitary pulmonary nodules (SPN) has constantly increased and the diagnosis accuracy of SPN has remained a hot topic in clinical and imaging diagnosis. The aim of this study was to evaluate the combination of low-dose spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm in the diagnosis of solitary pulmonary nodules (SPN). METHODS: 62 patients with SPN (42 cases of benign SPN and 20 cases of malignant SPN, pathology confirmed) were scanned by spectral CT with a dual-phase contrast-enhanced method. The iodine and water concentration (IC and WC) of the lesion and the artery in the image that had the same density were measured by the GSI (Gemstone Spectral Imaging) software. The normalized iodine and water concentration (NIC and NWC) of the lesion and the normalized iodine and water concentration difference (ICD and WCD) between the arterial and venous phases (AP and VP) were also calculated. The spectral HU (Hounsfield Unit ) curve was divided into 3 sections based on the energy (40–70, 70–100 and 100–140 keV) and the slopes (λHU) in both phases were calculated. The IC(AP), IC(VP), WC(AP) and WC(VP), NIC and NWC, and the λHU in benign and malignant SPN were compared by independent sample t-test. RESULTS: The iodine related parameters (IC(AP), IC(VP), NIC(AP), NIC(VP), and the ICD) of malignant SPN were significantly higher than that of benign SPN (t = 3.310, 1.330, 2.388, 1.669 and 3.251, respectively, P <0.05). The 3 λHU values of venous phase in malignant SPN were higher than that of benign SPN (t = 3.803, 2.846 and 3.205, P <0.05). The difference of water related parameters (WC(AP), WC(VP), NWC(AP), NWC(VP) and WCD) between malignant and benign SPN were not significant (t = 0.666, 0.257, 0.104, 0.550 and 0.585, P >0.05). CONCLUSIONS: The iodine related parameters and the slope of spectral curve are useful markers to distinguish the benign from the malignant lung diseases, and its application is extremely feasible in clinical applications. BioMed Central 2015-11-17 /pmc/articles/PMC4647278/ /pubmed/26576676 http://dx.doi.org/10.1186/s12880-015-0096-6 Text en © Xiao et al. 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Xiao, Huijuan
Liu, Yihe
Tan, Hongna
Liang, Pan
Wang, Bo
Su, Lei
Wang, Suya
Gao, Jianbo
A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules
title A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules
title_full A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules
title_fullStr A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules
title_full_unstemmed A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules
title_short A pilot study using low-dose Spectral CT and ASIR (Adaptive Statistical Iterative Reconstruction) algorithm to diagnose solitary pulmonary nodules
title_sort pilot study using low-dose spectral ct and asir (adaptive statistical iterative reconstruction) algorithm to diagnose solitary pulmonary nodules
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4647278/
https://www.ncbi.nlm.nih.gov/pubmed/26576676
http://dx.doi.org/10.1186/s12880-015-0096-6
work_keys_str_mv AT xiaohuijuan apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT liuyihe apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT tanhongna apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT liangpan apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT wangbo apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT sulei apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT wangsuya apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT gaojianbo apilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT xiaohuijuan pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT liuyihe pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT tanhongna pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT liangpan pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT wangbo pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT sulei pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT wangsuya pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules
AT gaojianbo pilotstudyusinglowdosespectralctandasiradaptivestatisticaliterativereconstructionalgorithmtodiagnosesolitarypulmonarynodules