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© 2021. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Purpose: 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.

Details

Title
Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography
Author
Rui-Yu, Lin; Fa-Jin Lv; Bin-Jie, Fu; Wang-Jia, Li; Zhang-Rui, Liang; Chu, Zhi-Gang
Pages
2933-2939
Section
Original Research
Publication year
2021
Publication date
2021
Publisher
Taylor & Francis Ltd.
e-ISSN
1178-7031
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2552265007
Copyright
© 2021. This work is licensed under https://creativecommons.org/licenses/by-nc/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.