A Spatiotemporal Transformer Framework on 4D-CT for Early Prediction of Progressive Pulmonary Fibrosis

Progressive pulmonary fibrosis (PPF) remains difficult to predict because static imaging may not fully capture regional respiratory motion, ventilation heterogeneity, and mechanical deformation. We propose a spatiotemporal transformer (ST-Former) framework that jointly models dynamic lung deformation, ventilation, and regional strain from four-dimensional computed tomography (4D-CT). The model integrates CT intensity, deformable registration-derived displacement magnitude, Jacobian-based ventilation maps, and Green-Lagrange strain magnitude maps into a unified four-channel spatiotemporal representation across ten respiratory phases. A total of 210 subjects were included in the final analytic cohort, comprising 130 patients with progressive pulmonary fibrosis and 80 patients with non-progressive interstitial lung disease. Progression was defined using a multidisciplinary reference standard based on ≥ 10% relative decline in forced vital capacity within approximately 12 months and/or radiological worsening. Across patient-level fivefold cross-validation, ST-Former achieved an AUC of 0.947 ± 0.016, outperforming ConvLSTM-3D (0.902 ± 0.021) and 3D ResNet-50 (0.861 ± 0.027). The model also demonstrated improved calibration, with a Brier score of 0.067 and an expected calibration error of 0.028. Center-stratified and leave-one-center-out analyses were performed to assess robustness to institutional domain shift. These results suggest that motion-informed spatiotemporal learning may provide a promising imaging biomarker for predicting pulmonary fibrosis progression, although prospective external validation is required before clinical translation.