# Cross-Modality Masked Learning for Survival Prediction in ICI Treated NSCLC Patients

arXiv: https://arxiv.org/abs/2507.06994 · HTML: https://mlsanity.com/paper/2507.06994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [Phenaki: Variable Length Video Generation From Open Domain Textual Description](https://mlsanity.com/paper/2210.02399/) (2022)
- [T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction](https://mlsanity.com/paper/2211.16887/) (2022)
- [Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data](https://mlsanity.com/paper/2107.00648/) (2021)
- [MoME: Mixture of Multimodal Experts for Cancer Survival Prediction](https://mlsanity.com/paper/2406.09696/) (2024)
- [Image-based Survival Analysis for Lung Cancer Patients using CNNs](https://mlsanity.com/paper/1808.09679/) (2018)
- [SurvRNC: Learning Ordered Representations for Survival Prediction using Rank-N-Contrast](https://mlsanity.com/paper/2403.10603/) (2024)
- [Multimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images](https://mlsanity.com/paper/2406.17225/) (2024)
- [Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations](https://mlsanity.com/paper/2407.17726/) (2024)

## Built on by (most cited first, 1 of 1)

- [CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction](https://mlsanity.com/paper/2607.08503/) (2026)

## Abstract (arXiv)

Accurate prognosis of non-small cell lung cancer (NSCLC) patients undergoing immunotherapy is essential for personalized treatment planning, enabling informed patient decisions, and improving both treatment outcomes and quality of life. However, the lack of large, relevant datasets and effective multi-modal feature fusion strategies pose significant challenges in this domain. To address these challenges, we present a large-scale dataset and introduce a novel framework for multi-modal feature fusion aimed at enhancing the accuracy of survival prediction. The dataset comprises 3D CT images and corresponding clinical records from NSCLC patients treated with immune checkpoint inhibitors (ICI), along with progression-free survival (PFS) and overall survival (OS) data. We further propose a cross-modality masked learning approach for medical feature fusion, consisting of two distinct branches, each tailored to its respective modality: a Slice-Depth Transformer for extracting 3D features from CT images and a graph-based Transformer for learning node features and relationships among clinical variables in tabular data. The fusion process is guided by a masked modality learning strategy, wherein the model utilizes the intact modality to reconstruct missing components. This mechanism improves the integration of modality-specific features, fostering more effective inter-modality relationships and feature interactions. Our approach demonstrates superior performance in multi-modal integration for NSCLC survival prediction, surpassing existing methods and setting a new benchmark for prognostic models in this context.