Integrating CT Radiomics and Machine Learning for Preoperative Assessment of STAS Grading and Prognostic...

The distance of spread through air spaces (STAS) dissemination is associated with prognosis in lung adenocarcinoma (LUAD). This study aimed to develop a computed tomography (CT) radiomics-based machine learning model to predict STAS grade.

Retrospective data included 293 LUAD patients for training/internal validation and 92 from the National Lung Screening Trial for external validation. STAS, defined as tumor cells beyond the tumor edge, was stratified by a 2500 µm threshold into grade I and II. Cox regression assessed associations between STAS grade and recurrence-free survival (RFS). Radiomics features were selected by minimum redundancy maximum relevance (MRMR), and 11 machine learning classifiers predicted STAS presence and grade. Independent clinic-radiological predictors were identified by univariable and multivariable analyses, and logistic regression combined these with the best radiomics model to build a combined model. Performance was evaluated by area under the curve (AUC) and accuracy (ACC).

STAS grade II was independently associated with poorer RFS (P = 0.037), while grade I was not. For STAS detection, the random forest model achieved AUCs of 0.888, 0.886 and 0.779 with accuracies of 81.5%, 85.2%, and 69.6%; the combined model performed similarly (AUCs 0.877, 0.894, 0.802; accuracies 77.6%, 85.2%, 72.8%). For STAS grading, logistic regression yielded AUCs of 0.845, 0.852, and 0.798 (accuracies 78.2%, 70.4%, 77.1%), with the combined model comparable (AUCs 0.845, 0.855, 0.796; accuracies 78.2%, 70.4%, 77.1%).

STAS grading improves prognostic stratification in LUAD. CT-based radiomics models allow noninvasive prediction of STAS grade, aiding surgical decisions and prognosis.