Predictive value of CT radiomics and inflammatory markers for pulmonary adenocarcinoma spread through air spaces.

Abstract

Objectives: To evaluate the predictive value of combining CT radiomics features and inflammatory markers for the preoperative prediction of spread through air spaces (STAS) in pulmonary adenocarcinoma.

Methods: In this retrospective study, we analyzed data from 256 patients diagnosed with pulmonary adenocarcinoma between 2021 and 2023. Patients were categorized into two groups based on the presence (n = 115) or absence (n = 141) of STAS, as confirmed by histopathological examination. CT imaging data and routine blood test results, including inflammatory markers, were collected. A validation cohort of 233 patients was included for external validation. Statistical analyses, including univariate and multivariate logistic regression, were performed to identify independent predictors of STAS. Model performance was assessed using Receiver Operating Characteristic curve analysis.

Results: Key CT radiomics features, such as density, satellite lesions, irregular shape, spiculation, vascular convergence, and the vacuole sign, were significantly associated with STAS. Among inflammatory markers, a lower lymphocyte-to-monocyte ratio (LMR) and higher neutrophil-to-lymphocyte (NLR) and platelet-to-lymphocyte ratios (PLR) were predictive of STAS. The combined predictive model, integrating CT radiomics and inflammatory markers, demonstrated a high discriminatory ability, achieving an area under the curve of 0.915, which was externally validated with an AUC of 0.847.

Conclusions: The combination of CT radiomics and inflammatory markers provides an effective, non-invasive preoperative tool for predicting STAS in pulmonary adenocarcinoma, aiding in early prognostication and treatment planning.