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DOI 10.2147/jir.s318125

Медицинская библиотека · Journal of inflammation research · 2021

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

Lin Rui-Yu, Lv Fa-Jin, Fu Bin-Jie, Li Wang-Jia, Liang Zhang-Rui, Chu Zhi-Gang

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Аннотация

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.

Для цитирования: Lin Rui-Yu, Lv Fa-Jin, Fu Bin-Jie, Li Wang-Jia, Liang Zhang-Rui, Chu Zhi-Gang. Features for Predicting Absorbable Pulmonary Solid Nodules as Depicted on Thin-Section Computed Tomography.. Journal of inflammation research. 2021. DOI: 10.2147/jir.s318125.
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