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Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules

OBJECTIVE: To retrospectively evaluate the value of computerized 3D texture analysis for differentiating pulmonary metastases from non-metastatic lesions in pediatric patients with osteosarcoma. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board. The study...

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Autores principales: Cho, Yeon Jin, Kim, Woo Sun, Choi, Young Hun, Ha, Ji Young, Lee, SeungHyun, Park, Sang Joon, Cheon, Jung-Eun, Kang, Hyoung Jin, Shin, Hee Young, Kim, In-One
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368316/
https://www.ncbi.nlm.nih.gov/pubmed/30735557
http://dx.doi.org/10.1371/journal.pone.0211969
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author Cho, Yeon Jin
Kim, Woo Sun
Choi, Young Hun
Ha, Ji Young
Lee, SeungHyun
Park, Sang Joon
Cheon, Jung-Eun
Kang, Hyoung Jin
Shin, Hee Young
Kim, In-One
author_facet Cho, Yeon Jin
Kim, Woo Sun
Choi, Young Hun
Ha, Ji Young
Lee, SeungHyun
Park, Sang Joon
Cheon, Jung-Eun
Kang, Hyoung Jin
Shin, Hee Young
Kim, In-One
author_sort Cho, Yeon Jin
collection PubMed
description OBJECTIVE: To retrospectively evaluate the value of computerized 3D texture analysis for differentiating pulmonary metastases from non-metastatic lesions in pediatric patients with osteosarcoma. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board. The study comprised 42 pathologically confirmed pulmonary nodules in 16 children with osteosarcoma who had undergone preoperative computed tomography between January 2009 and December 2014. Texture analysis was performed using an in-house program. Multivariate logistic regression analysis was performed to identify factors for differentiating metastatic nodules from non-metastases. A subgroup analysis was performed to identify differentiating parameters in small non-calcified pulmonary nodules. The receiver operator characteristic curve was created to evaluate the discriminating performance of the established model. RESULTS: There were 24 metastatic and 18 non-metastatic lesions. Multivariate analysis revealed that higher mean attenuation (adjusted odds ratio [OR], 1.014, P = 0.003) and larger effective diameter (OR, 1.745, P = 0.012) were significant differentiators. The analysis with small non-calcified pulmonary nodules (7 metastases and 18 non-metastases) revealed significant inter-group differences in various parameters. Logistic regression analysis revealed that higher mean attenuation (OR, 1.007, P = 0.008) was a significant predictor of non-calcified pulmonary metastases. The established logistic regression model of subgroups showed excellent discriminating performance in the ROC analysis (area under the curve, 0.865). CONCLUSION: Pulmonary metastases from osteosarcoma could be differentiated from non-metastases by using computerized texture analysis. Higher mean attenuation and larger diameter were significant predictors for pulmonary metastases, while higher mean attenuation was a significant predictor for small non-calcified pulmonary metastases.
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spelling pubmed-63683162019-02-22 Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules Cho, Yeon Jin Kim, Woo Sun Choi, Young Hun Ha, Ji Young Lee, SeungHyun Park, Sang Joon Cheon, Jung-Eun Kang, Hyoung Jin Shin, Hee Young Kim, In-One PLoS One Research Article OBJECTIVE: To retrospectively evaluate the value of computerized 3D texture analysis for differentiating pulmonary metastases from non-metastatic lesions in pediatric patients with osteosarcoma. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board. The study comprised 42 pathologically confirmed pulmonary nodules in 16 children with osteosarcoma who had undergone preoperative computed tomography between January 2009 and December 2014. Texture analysis was performed using an in-house program. Multivariate logistic regression analysis was performed to identify factors for differentiating metastatic nodules from non-metastases. A subgroup analysis was performed to identify differentiating parameters in small non-calcified pulmonary nodules. The receiver operator characteristic curve was created to evaluate the discriminating performance of the established model. RESULTS: There were 24 metastatic and 18 non-metastatic lesions. Multivariate analysis revealed that higher mean attenuation (adjusted odds ratio [OR], 1.014, P = 0.003) and larger effective diameter (OR, 1.745, P = 0.012) were significant differentiators. The analysis with small non-calcified pulmonary nodules (7 metastases and 18 non-metastases) revealed significant inter-group differences in various parameters. Logistic regression analysis revealed that higher mean attenuation (OR, 1.007, P = 0.008) was a significant predictor of non-calcified pulmonary metastases. The established logistic regression model of subgroups showed excellent discriminating performance in the ROC analysis (area under the curve, 0.865). CONCLUSION: Pulmonary metastases from osteosarcoma could be differentiated from non-metastases by using computerized texture analysis. Higher mean attenuation and larger diameter were significant predictors for pulmonary metastases, while higher mean attenuation was a significant predictor for small non-calcified pulmonary metastases. Public Library of Science 2019-02-08 /pmc/articles/PMC6368316/ /pubmed/30735557 http://dx.doi.org/10.1371/journal.pone.0211969 Text en © 2019 Cho et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Cho, Yeon Jin
Kim, Woo Sun
Choi, Young Hun
Ha, Ji Young
Lee, SeungHyun
Park, Sang Joon
Cheon, Jung-Eun
Kang, Hyoung Jin
Shin, Hee Young
Kim, In-One
Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules
title Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules
title_full Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules
title_fullStr Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules
title_full_unstemmed Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules
title_short Computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: Differentiation of pulmonary metastases from non-metastatic nodules
title_sort computerized texture analysis of pulmonary nodules in pediatric patients with osteosarcoma: differentiation of pulmonary metastases from non-metastatic nodules
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368316/
https://www.ncbi.nlm.nih.gov/pubmed/30735557
http://dx.doi.org/10.1371/journal.pone.0211969
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