Open Access
Open access
volume 15 issue 1 publication number 6

Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm

Publication typeJournal Article
Publication date2024-01-08
scimago Q1
wos Q1
SJR1.256
CiteScore6.6
Impact factor4.5
ISSN18694101
Radiology, Nuclear Medicine and imaging
Abstract
Objectives

To evaluate the clinical and non-contrast computed tomography (CT) features of patients with benign pulmonary subsolid nodules (SSNs) with a solid component ≤ 5 mm and their development trends via follow-up CT.

Methods

We retrospectively collected 436 data from patients who had SSNs with a solid component ≤ 5 mm, including 69 with absorbable benign SSNs (AB-SSNs), 70 with nonabsorbable benign SSNs (NB-SSNs), and 297 with malignant SSNs (M-SSNs). Models 1, 2, and 3 for distinguishing the different types of SSNs were then developed and validated.

Results

Patients with AB-SSNs were younger and exhibited respiratory symptoms more frequently than those with M-SSNs. The frequency of nodules detected during follow-up CT was in the following order: AB-SSNs > NB-SSNs > M-SSNs. NB-SSNs were smaller than M-SSNs, and ill-defined margins were more frequent in AB-SSNs than in NB-SSNs and M-SSNs. Benign SSNs exhibited irregular shape, target sign, and lower CT values more frequently compared to M-SSNs, whereas the latter demonstrated bubble lucency more commonly compared to the former. Furthermore, AB-SSNs showed more thickened interlobular septa and satellite lesions than M-SSNs and M-SSNs had more pleural retraction than AB-SSNs (all p < 0.017). The three models had AUCs ranging 0.748–0.920 and 0.790–0.912 in the training and external validation cohorts, respectively. A follow-up CT showed nodule progression in four benign SSNs.

Conclusions

The three SSN types have different clinical and imaging characteristics, with some benign SSNs progressing to resemble malignancy.

Critical relevance statement

A good understanding of the imaging features and development trends of benign SSNs may help reduce unnecessary follow-up or interventions. This retrospective study explores the CT characteristics of benign SSNs with a solid component ≤ 5 mm by comparing AB-SSNs, NB-SSNs, and M-SSNs and delineates their development trends via follow-up CT.

Key points

1. Different subsolid nodule types exhibit distinct clinical and imaging features.

2. A miniscule number of benign subsolid nodules can progress to resemble malignancy.

3. Knowing the clinical and imaging features and development trends of benign subsolid nodules can improve management.

Graphical Abstract
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GOST Copy
Wu S. et al. Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm // Insights into Imaging. 2024. Vol. 15. No. 1. 6
GOST all authors (up to 50) Copy
Wu S., Fan Xiao, Li X., Luo T., Li X., Li Q. Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm // Insights into Imaging. 2024. Vol. 15. No. 1. 6
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1186/s13244-023-01585-5
UR - https://doi.org/10.1186/s13244-023-01585-5
TI - Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm
T2 - Insights into Imaging
AU - Wu, Shun
AU - Fan Xiao
AU - Li, Xian
AU - Luo, Tian-you
AU - Li, Xing-Hua
AU - Li, Qi
PY - 2024
DA - 2024/01/08
PB - Springer Nature
IS - 1
VL - 15
PMID - 38191718
SN - 1869-4101
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2024_Wu,
author = {Shun Wu and Fan Xiao and Xian Li and Tian-you Luo and Xing-Hua Li and Qi Li},
title = {Clinical and non-contrast computed tomography characteristics and disease development in patients with benign pulmonary subsolid nodules with a solid component ≤ 5 mm},
journal = {Insights into Imaging},
year = {2024},
volume = {15},
publisher = {Springer Nature},
month = {jan},
url = {https://doi.org/10.1186/s13244-023-01585-5},
number = {1},
pages = {6},
doi = {10.1186/s13244-023-01585-5}
}