Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.15100 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915866670530560 |
|---|---|
| author | Caragliano, Alice Natalina Farina, Giulia Aksu, Fatih Caruso, Camillo Maria Tacconi, Claudia Greco, Carlo Nibid, Lorenzo Ippolito, Edy Fiore, Michele Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio |
| author_facet | Caragliano, Alice Natalina Farina, Giulia Aksu, Fatih Caruso, Camillo Maria Tacconi, Claudia Greco, Carlo Nibid, Lorenzo Ippolito, Edy Fiore, Michele Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_15100 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC Caragliano, Alice Natalina Farina, Giulia Aksu, Fatih Caruso, Camillo Maria Tacconi, Claudia Greco, Carlo Nibid, Lorenzo Ippolito, Edy Fiore, Michele Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio Computer Vision and Pattern Recognition 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. |
| title | Learning from Limited and Incomplete Data: A Multimodal Framework for Predicting Pathological Response in NSCLC |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.15100 |