用多模态模型预测肺癌新辅助治疗反应，不依赖完整数据。
Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC
- 结合影像与临床数据，自动处理缺失信息
- 在小样本下表现优于单一模态模型
- 适合真实临床中数据不全的场景
新辅助治疗后的主要病理反应（pR）是非小细胞肺癌（NSCLC）中具有临床意义的终点，与生存率提升密切相关。然而，在实际临床环境中因数据有限且临床资料不完整，准确预判pR仍具挑战。本研究提出一种多模态深度学习框架，通过基于基础模型的CT特征提取与缺失感知架构，实现小样本下的鲁棒学习，并显式建模缺失临床信息，无需传统插补方法。采用加权融合机制，发挥影像与临床模态的互补优势，所构建的多模态模型始终优于仅用影像或仅用临床数据的基线模型。结果表明，融合异构数据的价值显著，多模态、缺失感知系统在真实临床条件下具备支持pR预测的潜力。
原文摘要 · Abstract (English)
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.
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