# Survival Prediction of Brain Cancer with Incomplete Radiology, Pathology, Genomics, and Demographic Data

arXiv: https://arxiv.org/abs/2203.04419 · HTML: https://mlsanity.com/paper/2203.04419/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 8 Mar 2022.

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

- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Tensor Fusion Network for Multimodal Sentiment Analysis](https://mlsanity.com/paper/1707.07250/) (2017)
- [Breaking the Curse of Dimensionality with Convex Neural Networks](https://mlsanity.com/paper/1412.8690/) (2014)
- [Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis](https://mlsanity.com/paper/1912.08937/) (2019)
- [OL'E: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning](https://mlsanity.com/paper/1712.01727/) (2017)
- [Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data](https://mlsanity.com/paper/2107.00648/) (2021)
- [Automated Design of Deep Learning Methods for Biomedical Image Segmentation](https://mlsanity.com/paper/1904.08128/) (2019)
- [G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification](https://mlsanity.com/paper/2101.11656/) (2021)

## Built on by (most cited first, 25 of 25)

- [Deep Multimodal Learning with Missing Modality: A Survey](https://mlsanity.com/paper/2409.07825/) (2024)
- [Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Review](https://mlsanity.com/paper/2203.15588/) (2022)
- [Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects](https://mlsanity.com/paper/2311.02332/) (2023)
- [Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis](https://mlsanity.com/paper/2404.02394/) (2024)
- [Navigating the landscape of multimodal AI in medicine: a scoping review on technical challenges and clinical applications](https://mlsanity.com/paper/2411.03782/) (2024)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma](https://mlsanity.com/paper/2405.12963/) (2024)
- [Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis](https://mlsanity.com/paper/2502.15204/) (2025)
- [MMIST-ccRCC: A Real World Medical Dataset for the Development of Multi-Modal Systems](https://mlsanity.com/paper/2405.01658/) (2024)
- [Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations](https://mlsanity.com/paper/2407.17726/) (2024)
- [AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images](https://mlsanity.com/paper/2305.09946/) (2023)
- [CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object Detection](https://mlsanity.com/paper/2407.06780/) (2024)
- [M2Fusion: Bayesian-based Multimodal Multi-level Fusion on Colorectal Cancer Microsatellite Instability Prediction](https://mlsanity.com/paper/2401.07854/) (2024)
- [Genomics-guided Representation Learning for Pathologic Pan-cancer Tumor Microenvironment Subtype Prediction](https://mlsanity.com/paper/2406.06517/) (2024)
- [iMD4GC: Incomplete Multimodal Data Integration to Advance Precise Treatment Response Prediction and Survival Analysis for Gastric Cancer](https://mlsanity.com/paper/2404.01192/) (2024)
- [Multimodal Integration of Longitudinal Noninvasive Diagnostics for Survival Prediction in Immunotherapy Using Deep Learning](https://mlsanity.com/paper/2411.18253/) (2024)
- [Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations](https://mlsanity.com/paper/2503.10057/) (2025)
- [From Classification to Cross-Modal Understanding: Leveraging Vision-Language Models for Fine-Grained Renal Pathology](https://mlsanity.com/paper/2511.11984/) (2025)
- [SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction](https://mlsanity.com/paper/2607.09521/) (2026)
- [MDA: An Interpretable and Scalable Multi-Modal Fusion under Missing Modalities and Intrinsic Noise Conditions](https://mlsanity.com/paper/2406.10569/) (2024)
- [TMI-CLNet: Triple-Modal Interaction Network for Chronic Liver Disease Prognosis From Imaging, Clinical, and Radiomic Data Fusion](https://mlsanity.com/paper/2502.00695/) (2025)
- [AdaFuse: Adaptive Multimodal Fusion for Lung Cancer Risk Prediction via Reinforcement Learning](https://mlsanity.com/paper/2602.00347/) (2026)
- [Trimodal Glioma Representation Alignment via Volumetric Contrastive Learning](https://mlsanity.com/paper/2606.14568/) (2026)
- [ChronoSurv: A Clinical Pathway-Guided Graph Framework for Multimodal Survival Analysis](https://mlsanity.com/paper/2606.19140/) (2026)
- [ShapKO: Shapley-Adaptive Modality Knockout for Robust Multimodal Learning](https://mlsanity.com/paper/2607.09884/) (2026)

## Abstract (arXiv)

Integrating cross-department multi-modal data (e.g., radiological, pathological, genomic, and clinical data) is ubiquitous in brain cancer diagnosis and survival prediction. To date, such an integration is typically conducted by human physicians (and panels of experts), which can be subjective and semi-quantitative. Recent advances in multi-modal deep learning, however, have opened a door to leverage such a process to a more objective and quantitative manner. Unfortunately, the prior arts of using four modalities on brain cancer survival prediction are limited by a "complete modalities" setting (i.e., with all modalities available). Thus, there are still open questions on how to effectively predict brain cancer survival from the incomplete radiological, pathological, genomic, and demographic data (e.g., one or more modalities might not be collected for a patient). For instance, should we use both complete and incomplete data, and more importantly, how to use those data? To answer the preceding questions, we generalize the multi-modal learning on cross-department multi-modal data to a missing data setting. Our contribution is three-fold: 1) We introduce optimal multi-modal learning with missing data (MMD) pipeline with optimized hardware consumption and computational efficiency; 2) We extend multi-modal learning on radiological, pathological, genomic, and demographic data into missing data scenarios; 3) a large-scale public dataset (with 962 patients) is collected to systematically evaluate glioma tumor survival prediction using four modalities. The proposed method improved the C-index of survival prediction from 0.7624 to 0.8053.