Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.
Authors
Affiliations (15)
Affiliations (15)
- Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy. [email protected].
- Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden. [email protected].
- Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
- Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy.
- Anatomical Pathology Operative Research Unit, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy.
- Research Unit of Anatomical Pathology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
- Department of Engineering 'Enzo Ferrari', University of Modena and Reggio Emilia, Modena, Italy.
- Research Unit of Radiation Oncology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
- Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.
- Operative Research Unit of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy.
- Operative Research Unit of Radiology and Interventional Radiology, Fondazione Policlinico Universitario Campus Bio-Medico di Roma, Rome, Italy.
- University of Cassino and Southern Lazio, Cassino, Italy.
- Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy. [email protected].
- Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy. [email protected].
- Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering, Umeå University, Umeå, Sweden. [email protected].
Abstract
Accurate survival prediction in non-small cell lung cancer (NSCLC) requires integrating clinical, radiological, and histopathological data. Multimodal deep learning (MDL) can improve precision prognosis, but small cohorts and missing modalities limit its clinical applicability, as conventional approaches enforce complete-case filtering or imputation. We present a missing-aware multimodal survival framework that combines computed tomography (CT), whole-slide histopathology images (WSI), and structured clinical variables for overall survival modeling in unresectable stage II-III NSCLC. The framework uses foundation models (FMs) for modality-specific feature extraction and a missing-aware encoding strategy that enables intermediate multimodal fusion under naturally incomplete modality profiles. By design, the architecture processes all available data without dropping patients during training or inference. Intermediate fusion outperforms unimodal baselines and both early and late fusion strategies, with the trimodal configuration reaching a C-index of 74.42. Modality-importance analyses show that the fusion model adapts its reliance on each data stream according to representation informativeness, shaped by the alignment between FM pretraining objectives and the survival task. The learned risk scores produce clinically meaningful stratification of disease progression and metastatic risk, with statistically significant log-rank tests across all modality combinations, supporting the translational relevance of the proposed framework.