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Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study
Background: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. Methods: In a retrospective single-institutional study, 72 patients, who obtained serial CX...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297196/ https://www.ncbi.nlm.nih.gov/pubmed/37370955 http://dx.doi.org/10.3390/diagnostics13122060 |
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author | Lim, Chae Young Cha, Yoon Ki Chung, Myung Jin Park, Subin Park, Soyoung Woo, Jung Han Kim, Jong Hee |
author_facet | Lim, Chae Young Cha, Yoon Ki Chung, Myung Jin Park, Subin Park, Soyoung Woo, Jung Han Kim, Jong Hee |
author_sort | Lim, Chae Young |
collection | PubMed |
description | Background: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. Methods: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model. Results: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm(3) (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm(3) m(3) in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e., RMSE: 7975.6 mm(3), the smallest among the other variable parameters). Conclusions: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs. |
format | Online Article Text |
id | pubmed-10297196 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102971962023-06-28 Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study Lim, Chae Young Cha, Yoon Ki Chung, Myung Jin Park, Subin Park, Soyoung Woo, Jung Han Kim, Jong Hee Diagnostics (Basel) Article Background: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. Methods: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model. Results: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm(3) (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm(3) m(3) in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e., RMSE: 7975.6 mm(3), the smallest among the other variable parameters). Conclusions: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs. MDPI 2023-06-14 /pmc/articles/PMC10297196/ /pubmed/37370955 http://dx.doi.org/10.3390/diagnostics13122060 Text en © 2023 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 Lim, Chae Young Cha, Yoon Ki Chung, Myung Jin Park, Subin Park, Soyoung Woo, Jung Han Kim, Jong Hee Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study |
title | Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study |
title_full | Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study |
title_fullStr | Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study |
title_full_unstemmed | Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study |
title_short | Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study |
title_sort | estimating the volume of nodules and masses on serial chest radiography using a deep-learning-based automatic detection algorithm: a preliminary study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297196/ https://www.ncbi.nlm.nih.gov/pubmed/37370955 http://dx.doi.org/10.3390/diagnostics13122060 |
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