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

This 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.

We 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.

In 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).

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