Nodule subtype dependency of CT-based radiomics for preoperative prediction of spread through air spaces in stage I lung adenocarcinoma: a multi-centre study

Background: This study aimed to investigate the effects of nodule subtype on the performance of computed tomography (CT)-based radiomics for preoperative spread through air spaces (STAS) prediction in stage I invasive lung adenocarcinoma, by comparing a model developed across all subtypes with a model specific to pure-solid nodules.

Methods: This was a multi-centre retrospective study, with 1,377 patients in the internal cohort and 436 in the external validation cohorts. Two radiomics models were developed: an all-subtype model (M1) and a pure-solid-specific model (M2). Radiomic features were obtained from preoperative CT images, and model development was performed using Extreme Gradient Boosting. Predictive performance was assessed using the area under the curve (AUC), calibration analysis, and decision-curve analysis. In the external pure-solid cohort, the two models were directly compared using the DeLong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results: M1 yielded an AUC of 0.839 in the internal cohort and 0.865 in the overall external cohort. After external stratification by nodule subtype, the AUCs for non-solid and pure-solid nodules were 0.797 and 0.788, respectively. M2 yielded an AUC of 0.773 in the internal cohort and 0.768 in the external cohort. In the external pure-solid cohort, M1 exhibited superior discrimination (P=0.0018), while M2 yielded more favourable reclassification performance (NRI =0.643; IDI =0.184).

Conclusions: CT-based radiomics may have value for preoperative STAS prediction; however, its predictive performance likely depends on nodule subtype, with pure-solid nodules representing the primary diagnostic challenge.