Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC
This work addresses a critical problem for clinicians in oncology by improving prediction accuracy under real-world data constraints, though it is incremental in its approach.
The study tackled the challenge of predicting pathological response in non-small cell lung cancer from limited and incomplete clinical data by proposing a multimodal deep learning framework, which outperformed unimodal baselines.
Major pathological response (pR) following neoadjuvant therapy is a clinically meaningful endpoint in non-small cell lung cancer, strongly associated with improved survival. However, accurate preoperative prediction of pR remains challenging, particularly in real-world clinical settings characterized by limited data availability and incomplete clinical profiles. In this study, we propose a multimodal deep learning framework designed to address these constraints by integrating foundation model-based CT feature extraction with a missing-aware architecture for clinical variables. This approach enables robust learning from small cohorts while explicitly modeling missing clinical information, without relying on conventional imputation strategies. A weighted fusion mechanism is employed to leverage the complementary contributions of imaging and clinical modalities, yielding a multimodal model that consistently outperforms both unimodal imaging and clinical baselines. These findings underscore the added value of integrating heterogeneous data sources and highlight the potential of multimodal, missing-aware systems to support pR prediction under realistic clinical conditions.