# Preoperative Prediction of Spread Through Air Spaces in Lung Cancer Using 18F-FDG PET–Based Radiomics and Peritumoral Microenvironment Features

## TL;DR

This study shows that lung cancer's aggressive spread through air spaces can be predicted preoperatively using PET/CT imaging and radiomic analysis.

## Contribution

The study introduces a noninvasive method combining PET/CT radiomic and clinical features to predict STAS in lung cancer.

## Key findings

- Radiomic features from tumor and surrounding tissue are strongly linked to STAS.
- A combined clinicoradiomic model outperformed clinical and radiomic-only models in predicting STAS.
- Lower SUVmin_tumor and intratumoral SUV skewness are independent predictors of STAS.

## Abstract

Background/Objectives: Spread through air spaces (STAS) represents an aggressive invasion pattern in lung cancer and is associated with unfavorable oncologic outcomes. As STAS is currently identifiable only on postoperative pathology, reliable preoperative, noninvasive prediction remains a clinical challenge. This study aimed to evaluate the feasibility of predicting STAS using 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT)-derived radiomic and clinicoradiomic models. Methods: In this retrospective study, patients who underwent surgical resection for lung cancer with available preoperative 18F-FDG PET/CT imaging were analyzed. Radiomic features were extracted from intratumoral and peritumoral regions. Clinical, radiomic-only, and combined clinicoradiomic models were developed using LASSO-based feature selection and multivariable logistic regression. Model performance was evaluated using nested cross-validation, receiver operating characteristic analysis, calibration assessment, and decision curve analysis. Results: Radiomic features reflecting intratumoral metabolic characteristics and peritumoral tissue heterogeneity were significantly associated with STAS. The combined clinicoradiomic model demonstrated superior discriminative performance compared with the clinical and radiomic-only models (mean AUC ≈ 0.75), along with favorable calibration (Brier score = 0.20) and improved clinical net benefit across relevant threshold probabilities. Lower eosinophil count, lower SUVmin_tumor, and lower intratumoral SUV skewness emerged as independent predictors of STAS. Conclusions: Preoperative prediction of STAS in lung cancer is feasible using PET/CT-based radiomic analysis integrating intratumoral, peritumoral, and clinical features. This noninvasive approach provides biologically relevant information beyond conventional anatomical assessment and warrants further validation in prospective, multicenter cohorts.