Handling Missing Modalities in Multimodal Survival Prediction for Non-Small Cell Lung Cancer

TL;DR

This paper introduces a missing-aware multimodal survival prediction framework for NSCLC that effectively integrates incomplete clinical, radiological, and histopathological data using foundation models and a novel encoding strategy.

Contribution

It proposes a new architecture that handles missing modalities without dropping data, improving survival prediction accuracy in NSCLC.

Findings

Achieved a C-index of 74.42 with trimodal data.

Outperformed unimodal, early, and late fusion baselines.

Produced clinically meaningful risk stratification.

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.