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

Alice Natalina Caragliano; Giulia Farina; Fatih Aksu; Camillo Maria Caruso; Claudia Tacconi; Carlo Greco; Lorenzo Nibid; Edy Ippolito; Michele Fiore; Giuseppe Perrone; Sara Ramella; Paolo Soda; Valerio Guarrasi

arXiv · 2603.15100

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

Alice Natalina Caragliano·Giulia Farina·Fatih Aksu·Camillo Maria Caruso·Claudia Tacconi·Carlo Greco·Lorenzo Nibid·Edy Ippolito·Michele Fiore·Giuseppe Perrone·Sara Ramella·Paolo Soda·Valerio Guarrasi

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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.

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Alice Natalina Caragliano, Giulia Farina, Fatih Aksu, Camillo Maria Caruso, Claudia Tacconi, Carlo Greco, Lorenzo Nibid, Edy Ippolito, Michele Fiore, Giuseppe Perrone, Sara Ramella, Paolo Soda, Valerio Guarrasi. 2026-03-16. Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC. https://arxiv.org/abs/2603.15100

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