This abstract presents a multimodal deep learning framework that integrates clinical variables, imaging-derived radiomic features, and large language model insights to predict postoperative complications in lung cancer surgery. The model generates editable, clinician-friendly risk summaries so that surgical experts can refine risk predictions in real time.