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

2026-02-11

Lu Yu, Shenlun Chen, Junyi Li, HeQing Yi, Jin Wang, Jianjiao Ni, Xiaoli Zheng, Hong Ge, Zhengfei Zhu, Ligang Xing, Petros Kalendralis, Leonard Wee, Andre Dekker, Zhen Zhang, Zhaoxiang Ye, Zhiyong Yuan,
PET/CT-based deep learning model predicts distant metastasis after SBRT for early-stage NSCLC: A multicenter study,
Computerized Medical Imaging and Graphics,
Volume 126,
2025,
102663,
ISSN 0895-6111,
https://doi.org/10.1016/j.compmedimag.2025.102663.
(https://www.sciencedirect.com/science/article/pii/S0895611125001727)
Abstract: 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.
Keywords: Non-small cell lung cancer; Stereotactic body radiotherapy; PET/CT; Deep learning; Distant metastasis