Objective To construct a structural risk prediction model for visceral pleural invasion in early lung adenocarcinoma based on computed tomography (CT) features.

Methods 170 patients with early lung adenocarcinoma treated surgically in our hospital were retrospectively selected between January 2016 and December 2022 and grouped according into invasion (84 cases) and non-invasion (86 cases) groups. Independent risk factors related to CT imaging features of visceral pleural invasion in early lung adenocarcinoma were evaluated using univariate and multivariate factor methods. The effectiveness of the CT imaging feature prediction model for visceral pleural invasion in early subpleural lung adenocarcinoma was analyzed.

Results Univariate analysis showed that the proportion and maximum diameter of solid components, bridging sign, vascular bundle sign, and relationship between the lesion and pleura may be related to visceral pleural invasion in patients with early subpleural lung adenocarcinoma. Multivariate analysis showed that the bridging sign, vascular bundle sign, and type II/III relationship between the lesion and pleura were independent risk factors for visceral pleural invasion in early subpleural lung adenocarcinoma. The best cutoff values for predicting visceral pleural invasion in early subpleural lung adenocarcinoma using the bridge sign, vascular bundle sign, adjacent relationship between the lesion and pleura, and logistic model prediction probability were 0.50, 0.50, 0.50, and 56.25％, respectively. The Jordan index for each of these was 24.00％, 23.95％, 48.70％, and 55.04％, respectively.

Conclusion Based on the bridge sign, vascular cluster sign, and the relationship between tumor and pleural adjacency, the CT imaging feature model can be used to identify high-risk groups for visceral pleural invasion in early lung adenocarcinoma.