Evidence map›Paper›PMID 42625155›Full record

ArticleBMC medical imaging2026

Assessment of risk factors in persistent subsolid pulmonary nodules based on CT dynamic follow-up.

Fei Li, Jinlong Liu, Xiangyang Ren, Zihao Chen, Shenyu Yang, Yuanbo Ma, Yaman Li, Yufang Du, Xiaopeng Yang

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Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Fei Li *The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jinlong Liu *The Affiliated Hospital of Hebei University of Engineering, Hebei, China.
Xiangyang RenThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Zihao ChenThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Shenyu YangThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yuanbo MaThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yaman LiThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Yufang DuThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Xiaopeng YangThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. 13837141925@163.com.ORCID https://orcid.org/0000-0002-2122-5490

Funding

the Henan Province Key Research and Development Program No. z241111311000the National Natural Science Foundation of China No. 62206251
6 · The paper itself

Abstract

backgroundThis study was designed to employ CT-based dynamic follow-up and semi-automated segmentation to investigate the growth rate of SSNs and identify risk factors for their progression.

methodsWe retrospectively analyzed 665 SSN patients from the NLST and 176 from the First Affiliated Hospital of Zhengzhou University. Semi-automated segmentation software measured the mean diameter, volume, and overall mean CT attenuation (m-CTA) of all nodules. Nodule mass, diameter, volume, and mass doubling time were further calculated. Based on follow-up results, nodules were divided into cancer and non-cancer groups to compare growth rates and identify the optimal method for detecting malignancy-related growth. All SSNs were further divided into growth and non-growth groups based on selected measurements for comparison of clinical and radiological features. Independent predictors of SSN growth were evaluated through Kaplan-Meier analysis and Multivariate Cox regression modeling.

resultsIn both datasets, significant differences (p < 0.05) were identified in all quantitative parameters including diameter, volume and mass between the cancer and non-cancer groups. Volume and its doubling time demonstrated the smallest P-values (p < 0.001), indicating that volumetric assessment provides the most sensitive measurement for detecting tumor growth. Multivariable Cox regression analysis showed that age, pleural indentation, and initial m-CTA were independent risk factors for the growth of SSNs in both datasets (all p < 0.05), while nodule type was significantly associated with growth only in the NLST dataset (p < 0.001).

conclusionsAdvanced age, pleural indentation sign, and a higher initial m-CTA attenuation are independent risk factors for the growth of SSNs.

Indexed as

Lung NeoplasmsMultiple Pulmonary NodulesSolitary Pulmonary NoduleTomography, X-Ray ComputedAgedDisease ProgressionFemaleFollow-Up StudiesHumansMaleMiddle AgedRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesRisk FactorsComputed tomographyGrowth predictionSubsolid nodules

Identifiers

PMID42625155
PMCPMC13495464

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