Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography

Rui-Yu Lin,&ast; Fa-Jin Lv,&ast; Bin-Jie Fu, Wang-Jia Li, Zhang-Rui Liang, Zhi-Gang Chu Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhi-Gang...

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Autores principales:	Lin RY, Lv FJ, Fu BJ, Li WJ, Liang ZR, Chu ZG
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	solid nodule absorbable nodule follow-up tomography x-ray computed Pathology RB1-214 Therapeutics. Pharmacology RM1-950
Acceso en línea:	https://doaj.org/article/b5d0ffbaec1d48dc9b1cae4885c33d37
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spelling	oai:doaj.org-article:b5d0ffbaec1d48dc9b1cae4885c33d372021-12-02T16:32:15ZFeatures for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography1178-7031https://doaj.org/article/b5d0ffbaec1d48dc9b1cae4885c33d372021-07-01T00:00:00Zhttps://www.dovepress.com/features-for-predicting-absorbable-pulmonary-solid-nodules-as-depicted-peer-reviewed-fulltext-article-JIRhttps://doaj.org/toc/1178-7031Rui-Yu Lin,&ast; Fa-Jin Lv,&ast; Bin-Jie Fu, Wang-Jia Li, Zhang-Rui Liang, Zhi-Gang Chu Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhi-Gang ChuDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, People’s Republic of ChinaTel +86 18723032809Fax +86 23 68811487Email chuzg0815@163.comPurpose: To investigate the clinical and computed tomography (CT) characteristics of absorbable pulmonary solid nodules (PSNs) and to clarify CT features for distinguishing absorbable PSNs from malignant ones.Materials and Methods: From January 2015 to February 2021, a total of 316 patients with 348 PSNs (171 absorbable and 177 size-matched malignant) were retrospectively enrolled. Their clinical and CT data were analyzed and compared to determine CT features for predicting absorbable PSNs.Results: Between absorbable and malignant PSNs, there were significant differences in patients’ age, lesions’ locations, shapes, homogeneity, borders, distance from the pleura, vacuoles, air bronchograms, lobulation, spiculation, halo sign, multiple concomitant nodules and pleural indentation (each P < 0.05). Multivariate analysis revealed that the independent predictors of absorbable PSNs were the following: patient age ≤ 55 years (OR, 2.660; 95% CI, 1.432– 4.942; P = 0.002), homogeneous density (OR, 2.487; 95% CI, 1.107– 5.590; P = 0.027), ill-defined border (OR, 5.445; 95% CI, 1.661– 17.846; P = 0.005), halo sign (OR, 3.135; 95% CI, 1.154– 8.513; P = 0.025), multiple concomitant nodules (OR, 8.700; 95% CI, 4.401– 17.197; P< 0.001), and abutting pleura (OR, 3.759; 95% CI, 1.407– 10.044; P = 0.008). The indicators for malignant PSNs were the following: lobulation (OR, 3.904; 95% CI, 1.956– 7.791; P< 0.001), spiculation (OR, 4.980; 95% CI, 2.202– 11.266, P< 0.001), and pleural indentation (OR, 4.514; 95% CI, 1.223– 16.666; P = 0.024).Conclusion: In patients younger than 55 years, PSNs with homogeneous density, ill-defined border, halo sign, multiple concomitant nodules, and abutting pleura should be highly suspected as absorbable ones.Keywords: solid nodule, absorbable nodule, follow-up, tomography, x-ray computedLin RYLv FJFu BJLi WJLiang ZRChu ZGDove Medical Pressarticlesolid noduleabsorbable nodulefollow-uptomographyx-ray computedPathologyRB1-214Therapeutics. PharmacologyRM1-950ENJournal of Inflammation Research, Vol Volume 14, Pp 2933-2939 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	solid nodule absorbable nodule follow-up tomography x-ray computed Pathology RB1-214 Therapeutics. Pharmacology RM1-950
spellingShingle	solid nodule absorbable nodule follow-up tomography x-ray computed Pathology RB1-214 Therapeutics. Pharmacology RM1-950 Lin RY Lv FJ Fu BJ Li WJ Liang ZR Chu ZG Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
description	Rui-Yu Lin,&ast; Fa-Jin Lv,&ast; Bin-Jie Fu, Wang-Jia Li, Zhang-Rui Liang, Zhi-Gang Chu Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhi-Gang ChuDepartment of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, People’s Republic of ChinaTel +86 18723032809Fax +86 23 68811487Email chuzg0815@163.comPurpose: To investigate the clinical and computed tomography (CT) characteristics of absorbable pulmonary solid nodules (PSNs) and to clarify CT features for distinguishing absorbable PSNs from malignant ones.Materials and Methods: From January 2015 to February 2021, a total of 316 patients with 348 PSNs (171 absorbable and 177 size-matched malignant) were retrospectively enrolled. Their clinical and CT data were analyzed and compared to determine CT features for predicting absorbable PSNs.Results: Between absorbable and malignant PSNs, there were significant differences in patients’ age, lesions’ locations, shapes, homogeneity, borders, distance from the pleura, vacuoles, air bronchograms, lobulation, spiculation, halo sign, multiple concomitant nodules and pleural indentation (each P < 0.05). Multivariate analysis revealed that the independent predictors of absorbable PSNs were the following: patient age ≤ 55 years (OR, 2.660; 95% CI, 1.432– 4.942; P = 0.002), homogeneous density (OR, 2.487; 95% CI, 1.107– 5.590; P = 0.027), ill-defined border (OR, 5.445; 95% CI, 1.661– 17.846; P = 0.005), halo sign (OR, 3.135; 95% CI, 1.154– 8.513; P = 0.025), multiple concomitant nodules (OR, 8.700; 95% CI, 4.401– 17.197; P< 0.001), and abutting pleura (OR, 3.759; 95% CI, 1.407– 10.044; P = 0.008). The indicators for malignant PSNs were the following: lobulation (OR, 3.904; 95% CI, 1.956– 7.791; P< 0.001), spiculation (OR, 4.980; 95% CI, 2.202– 11.266, P< 0.001), and pleural indentation (OR, 4.514; 95% CI, 1.223– 16.666; P = 0.024).Conclusion: In patients younger than 55 years, PSNs with homogeneous density, ill-defined border, halo sign, multiple concomitant nodules, and abutting pleura should be highly suspected as absorbable ones.Keywords: solid nodule, absorbable nodule, follow-up, tomography, x-ray computed
format	article
author	Lin RY Lv FJ Fu BJ Li WJ Liang ZR Chu ZG
author_facet	Lin RY Lv FJ Fu BJ Li WJ Liang ZR Chu ZG
author_sort	Lin RY
title	Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
title_short	Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
title_full	Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
title_fullStr	Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
title_full_unstemmed	Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
title_sort	features for predicting absorbable pulmonary solid nodules as depicted on thin-section computed tomography
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/b5d0ffbaec1d48dc9b1cae4885c33d37
work_keys_str_mv	AT linry featuresforpredictingabsorbablepulmonarysolidnodulesasdepictedonthinsectioncomputedtomography AT lvfj featuresforpredictingabsorbablepulmonarysolidnodulesasdepictedonthinsectioncomputedtomography AT fubj featuresforpredictingabsorbablepulmonarysolidnodulesasdepictedonthinsectioncomputedtomography AT liwj featuresforpredictingabsorbablepulmonarysolidnodulesasdepictedonthinsectioncomputedtomography AT liangzr featuresforpredictingabsorbablepulmonarysolidnodulesasdepictedonthinsectioncomputedtomography AT chuzg featuresforpredictingabsorbablepulmonarysolidnodulesasdepictedonthinsectioncomputedtomography
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Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography

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