PET/CT-based deep learning model predicts distant metastasis after SBRT for early-stage NSCLC: A multicenter study

Distant metastasis (DM) is the most frequent recurrence mode following stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC). Assessing DM risk prior to treatment initiation is critical. This study aimed to develop and validate a deep learning fusion model, based on 18F-FDG PET/CT images, to predict DM risk. A total of 566 patients from 5 hospitals were allocated into a training set (n = 347), an internal test set (n = 139), and an external test set (n = 80). Deep learning features were extracted from CT, PET, and fusion images using a variational autoencoder. Metastasis-free survival prognostic models were developed via fully connected networks. The fusion model demonstrated superior predictive capability compared to the CT or PET models alone, achieving C-indices of 0.864 (training), 0.819 (internal test), and 0.782 (external test). The model successfully stratified patients into high- and low-risk groups with significantly differentiated MFS (e.g., training set: HR=8.425, p < 0.001; internal test set, HR=6.828, p < 0.001; external test set: HR=4.376, p = 0.011). It was identified as an independent prognostic factor for MFS (HR=14.387, p < 0.001). In conclusions, the 18F-FDG PET/CT deep learning-based fusion model provides a robust prediction of distant metastasis risk and MFS in early-stage NSCLC patients receiving SBRT. This tool may offer objective data to inform individualized treatment decisions.