Multimodal Fusion of Imaging and Genomics for Lung Cancer Recurrence Prediction
Open accessAbstract
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.
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When there are disproportionately few samples relative to the number of features, or when some input features are sparsely represented, early or late fusion is preferred because they can be implemented with traditional machine learning algorithms better suited to such data.
“For example, if there are disproportionately few samples relative to the number of features in the dataset or if some of the input features are sparsely represented, early or late fusion is preferred because they can be implemented with traditional machine learning algorithms (e.g., Lasso and ElasticNet47) that are better suited for this type of data48.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [48]
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Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · see citing works
“…to the number of features in the dataset or if some of the input features are sparsely represented, early or late fusion is preferred because they can be implemented with traditional machine learning algorithms (e.g., Lasso and ElasticNet 47 ) that are better suited for this type of data 48 .”
MethodMultimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
A systematic review of intermediate fusion in multimodal deep learning for biomedical applications
Guarrasi, V., Aksu, F., Caruso, C. M., Di Feola, F., Rofena, A., Ruffini, F., & Soda, P. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105509 · Cited by 95 · see citing works
Simon, B. D., Ozyoruk, K. B., Gelikman, D. G., Harmon, S. A., & Türkbey, B. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242631 · Cited by 73 · see citing works
Modeling Uncertainty in Multi-Modal Fusion for Lung Cancer Survival Analysis
Wang, H., Subramanian, V., & Syeda-Mahmood, T. (2021) · 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9433823 · Cited by 26 · see citing works
Self-citation
- Authors
- Subramanian, V., Do, M. N., & Syeda-Mahmood, T.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- IBM Research - Almaden (US)
- Venue
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
- Publisher
- IEEE
- Published
- 2020-04
- Pages
- 804-808
- OpenAlex
- W3005105526
- Cited by
- 42
- References
- 0
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 10 · 2023: 6 · 2022: 8 · 2021: 7 · 2020: 1
- FWCI
- 6.02
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE RESOURCE CENTERS
- Funders
- Center for Cognitive Computing Systems Research
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- multimodal fusion, radiogenomics, computed tomography, non-small cell lung cancer, Cox proportional hazards model, elastic net regularization, postoperative recurrence, early-stage lung cancer
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