Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC

TL;DR

This paper introduces a multimodal deep learning framework that combines CT imaging and clinical data, effectively predicting pathological response in NSCLC patients despite limited and incomplete data scenarios.

Contribution

It presents a novel missing-aware architecture and fusion mechanism that improve prediction accuracy using heterogeneous data sources without traditional imputation.

Findings

Multimodal model outperforms unimodal baselines.

Effective learning from small, incomplete datasets.

Highlights the value of integrating imaging and clinical data.

Abstract

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.