Individualised prediction of progression of solitary sub-solid pulmonary nodules based on CT semantic and clinical features: a 3-year follow-up study.

ABSTRACT

AIM: To develop and validate a progressive prediction model for estimating the time to progression (TTP) of sub-solid pulmonary nodules (SSNs). MATERIALS AND METHODS: A total of 126 cases who met inclusion and exclusion criteria were included in the study. The primary endpoint of the study was TTP of SSNs. Baseline characteristics were assessed in terms of clinical and CT semantic features. Kaplan-Meier analysis and Cox regression analysis were performed to determine the relationship between SSNs TTP and factors from the entire data set. The nomogram was constructed based on the result of multivariate analysis and internal validation was performed using the bootstrapping. The nomogram's performance was assessed with the C-index, calibration curves, and decision curve analysis. RESULTS: The median follow-up time of the population was 42.5 (21.5) months. On Kaplan-Meier analysis, patients with higher or positive values of the indices had higher cumulative progression rates (p<0.05). Multivariate Cox regression models identified diameter, consolidation tumour ratio (CTR), morphology, and vasodilation sign (VDS) as independent risk factors of TTP. These predictors were included in the final model to estimate individual probabilities of progression in the 3 years, which performed well in the discrimination (the C-index was 0.901 [95%CI 0.830-0.981] and 0.875 [95%CI 0.805-0.942] in the training and internally validation sets). CONCLUSION: The radiological semantic features nomogram is a promising and favourable prognostic biomarker for predicting progression and may aid in clinical risk stratification and decision-making for SSNs.