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Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT

BACKGROUND: Lung lobar ventilation and perfusion (V/Q) quantification is generally obtained by generating planar scintigraphy images and then imposing three equally sized regions of interest on the data of each lung. This method is fast but not as accurate as SPECT/CT imaging, which provides three-d...

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Autores principales: Verrecchia-Ramos, Emilie, Morel, Olivier, Ginet, Merwan, Retif, Paul, Ben Mahmoud, Sinan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513978/
https://www.ncbi.nlm.nih.gov/pubmed/37733103
http://dx.doi.org/10.1186/s40658-023-00578-z
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author Verrecchia-Ramos, Emilie
Morel, Olivier
Ginet, Merwan
Retif, Paul
Ben Mahmoud, Sinan
author_facet Verrecchia-Ramos, Emilie
Morel, Olivier
Ginet, Merwan
Retif, Paul
Ben Mahmoud, Sinan
author_sort Verrecchia-Ramos, Emilie
collection PubMed
description BACKGROUND: Lung lobar ventilation and perfusion (V/Q) quantification is generally obtained by generating planar scintigraphy images and then imposing three equally sized regions of interest on the data of each lung. This method is fast but not as accurate as SPECT/CT imaging, which provides three-dimensional data and therefore allows more precise lobar quantification. However, the manual delineation of each lobe is time-consuming, which makes SPECT/CT incompatible with the clinical workflow for V/Q estimation. An alternative may be to use artificial intelligence-based auto-segmentation tools such as AutoLung3D (Siemens Healthineers, Knoxville, USA), which automatically delineate the lung lobes on the CT data acquired with the SPECT data. The present study assessed the clinical validity of this approach relative to planar scintigraphy and manual quantification in SPECT/CT. METHODS: The Autolung3D software was tested on the retrospective SPECT/CT data of 43 patients who underwent V/Q scintigraphy with (99m)Tc-macroaggregated albumin and (99m)Tc-labeled aerosol. It was compared to planar scintigraphy and SPECT/CT using the manual quantification method in terms of relative lobar V/Q quantification values and interobserver variability. RESULTS: The three methods provided similar V/Q estimates for the left lung lobes and total lungs. However, compared to the manual SPECT/CT method, planar scintigraphy yielded significantly higher estimates for the middle right lobe and significantly lower estimates for the superior and inferior right lobes. The estimates of the manual and automated SPECT/CT methods were similar. However, the post-processing time in the automated method was approximately 5 min compared to 2 h for the manual method. Moreover, the automated method associated with a drastic reduction in interobserver variability: Its maximal relative standard deviation was only 5%, compared to 23% for planar scintigraphy and 19% for the manual SPECT/CT method. CONCLUSIONS: This study validated the AutoLung3D software for general clinical use since it rapidly provides accurate lobar quantification in V/Q scans with markedly less interobserver variability than planar scintigraphy or the manual SPECT/CT method.
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spelling pubmed-105139782023-09-23 Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT Verrecchia-Ramos, Emilie Morel, Olivier Ginet, Merwan Retif, Paul Ben Mahmoud, Sinan EJNMMI Phys Original Research BACKGROUND: Lung lobar ventilation and perfusion (V/Q) quantification is generally obtained by generating planar scintigraphy images and then imposing three equally sized regions of interest on the data of each lung. This method is fast but not as accurate as SPECT/CT imaging, which provides three-dimensional data and therefore allows more precise lobar quantification. However, the manual delineation of each lobe is time-consuming, which makes SPECT/CT incompatible with the clinical workflow for V/Q estimation. An alternative may be to use artificial intelligence-based auto-segmentation tools such as AutoLung3D (Siemens Healthineers, Knoxville, USA), which automatically delineate the lung lobes on the CT data acquired with the SPECT data. The present study assessed the clinical validity of this approach relative to planar scintigraphy and manual quantification in SPECT/CT. METHODS: The Autolung3D software was tested on the retrospective SPECT/CT data of 43 patients who underwent V/Q scintigraphy with (99m)Tc-macroaggregated albumin and (99m)Tc-labeled aerosol. It was compared to planar scintigraphy and SPECT/CT using the manual quantification method in terms of relative lobar V/Q quantification values and interobserver variability. RESULTS: The three methods provided similar V/Q estimates for the left lung lobes and total lungs. However, compared to the manual SPECT/CT method, planar scintigraphy yielded significantly higher estimates for the middle right lobe and significantly lower estimates for the superior and inferior right lobes. The estimates of the manual and automated SPECT/CT methods were similar. However, the post-processing time in the automated method was approximately 5 min compared to 2 h for the manual method. Moreover, the automated method associated with a drastic reduction in interobserver variability: Its maximal relative standard deviation was only 5%, compared to 23% for planar scintigraphy and 19% for the manual SPECT/CT method. CONCLUSIONS: This study validated the AutoLung3D software for general clinical use since it rapidly provides accurate lobar quantification in V/Q scans with markedly less interobserver variability than planar scintigraphy or the manual SPECT/CT method. Springer International Publishing 2023-09-21 /pmc/articles/PMC10513978/ /pubmed/37733103 http://dx.doi.org/10.1186/s40658-023-00578-z Text en © The Author(s) 2023 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 Original Research
Verrecchia-Ramos, Emilie
Morel, Olivier
Ginet, Merwan
Retif, Paul
Ben Mahmoud, Sinan
Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT
title Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT
title_full Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT
title_fullStr Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT
title_full_unstemmed Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT
title_short Clinical validation of an AI-based automatic quantification tool for lung lobes in SPECT/CT
title_sort clinical validation of an ai-based automatic quantification tool for lung lobes in spect/ct
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513978/
https://www.ncbi.nlm.nih.gov/pubmed/37733103
http://dx.doi.org/10.1186/s40658-023-00578-z
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