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

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