# Multimodal fusion of imaging and genomics for lung cancer recurrence prediction

arXiv: https://arxiv.org/abs/2002.01982 · HTML: https://mlsanity.com/paper/2002.01982/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data](https://mlsanity.com/paper/2107.00648/) (2021)
- [Data-Centric Foundation Models in Computational Healthcare: A Survey](https://mlsanity.com/paper/2401.02458/) (2024)
- [A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications](https://mlsanity.com/paper/2408.02686/) (2024)
- [Multimodal fusion using sparse CCA for breast cancer survival prediction](https://mlsanity.com/paper/2103.05432/) (2021)
- [Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis](https://mlsanity.com/paper/2210.14377/) (2022)
- [Multi-modality fusion using canonical correlation analysis methods: Application in breast cancer survival prediction from histology and genomics](https://mlsanity.com/paper/2111.13987/) (2021)
- [MARL: Multimodal Attentional Representation Learning for Disease Prediction](https://mlsanity.com/paper/2105.00310/) (2021)
- [Multimodal Machine Learning in Precision Health](https://mlsanity.com/paper/2204.04777/) (2022)
- [Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small-Cell Lung Cancer](https://mlsanity.com/paper/2104.14420/) (2021)
- [MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction](https://mlsanity.com/paper/2307.07093/) (2023)
- [MeD-3D: A Multimodal Deep Learning Framework for Precise Recurrence Prediction in Clear Cell Renal Cell Carcinoma (ccRCC)](https://mlsanity.com/paper/2507.07839/) (2025)

## Abstract (arXiv)

Lung cancer has a high rate of recurrence in early-stage patients. Predicting the post-surgical recurrence in lung cancer patients has traditionally been approached using single modality information of genomics or radiology images. We investigate the potential of multimodal fusion for this task. By combining computed tomography (CT) images and genomics, we demonstrate improved prediction of recurrence using linear Cox proportional hazards models with elastic net regularization. We work on a recent non-small cell lung cancer (NSCLC) radiogenomics dataset of 130 patients and observe an increase in concordance-index values of up to 10%. Employing non-linear methods from the neural network literature, such as multi-layer perceptrons and visual-question answering fusion modules, did not improve performance consistently. This indicates the need for larger multimodal datasets and fusion techniques better adapted to this biological setting.