A Foundation Model Based CT Biomarker for Non‐Invasive Prediction of Response to Neoadjuvant Immunochemotherapy in Non‐Small Cell Lung Cancer

ABSTRACT Predicting pathological complete response (pCR) to neoadjuvant immunochemotherapy in non‐small cell lung cancer (NSCLC) is clinically important yet remains challenging. Here, we introduce a foundation model‐derived computed tomography (CT) imaging biomarker established from a multi‐center cohort of 702 patients. Specifically, we developed and validated a non‐invasive baseline CT‐based model for risk stratification of pathological response. To address scanner and protocol heterogeneity, we first built a 3D Vision Mamba‐based CT super‐resolution model trained on 2494 cases for image standardization. We then fine‐tuned a lung cancer‐specific CT foundation model from a pretrained 3D model (VoCo) using 6643 chest CT scans. Finally, we constructed a multi‐task Swin Transformer that jointly performs risk stratification and segments tumors to generate the imaging biomarker. Across five centers, the model achieved consistently strong generalization (AUC: 0.75–0.87) for pCR prediction. Genomic analysis revealed that the biomarker was independent of tumor mutational burden but significantly associated with TP53 mutations, suggesting an association with a radiogenomic phenotype related to this alteration. Together, these results demonstrate a generalizable and biologically meaningful foundation model‐based biomarker for non‐invasive risk stratification of pathological response in NSCLC.