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Multimodal data fusion for cancer biomarker discovery with deep learning
Open accessAbstract
Technological advances have made it possible to study a patient from multiple angles with high-dimensional, high-throughput multiscale biomedical data. In oncology, massive amounts of data are being generated, ranging from molecular, histopathology, radiology to clinical records. The introduction of deep learning has greatly advanced the analysis of biomedical data. However, most approaches focus on single data modalities, leading to slow progress in methods to integrate complementary data types. Development of effective multimodal fusion approaches is becoming increasingly important as a single modality might not be consistent and sufficient to capture the heterogeneity of complex diseases to tailor medical care and improve personalized medicine. Many initiatives now focus on integrating these disparate modalities to unravel the biological processes involved in multifactorial diseases such as cancer. However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability and standardization of datasets. Cancer diagnosis and treatment decisions often focus on one data source. Steyaert and colleagues discuss the current status and challenges of data fusion, including electronic health records, molecular data, digital pathology and radiographic images, in cancer research and translational development.
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“NLU is central to enhancing the comprehension and contextualization of human language in AI systems, and involves key capabilities such as semantic analysis, named entity recognition, sentiment analysis, textual entailment, and machine reading comprehension [190], [191], [192], [193].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [192]
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“AI systems can profit significantly from assimilating multimodal data into prediction and classification models to imitate integrative human clinical decision-making.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [14]
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“This can boost their robustness and accuracy, enable the discovery of new biomarkers and therapeutic targets6,14, as well as improve model performance15–17.”
From Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging (2024) · cites this paper as [14]
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“The understanding of important gene features or cross-modality regulatory networks is essential to uncover the mechanism of disease development and identify new disease biomarkers and drug targets [28].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [28]
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“The high costs of biomedical data generation, difficulties in patient follow-up, and privacy restrictions limit the size of training datasets and lead to missing data modalities [28].”
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“The data generated by these diverse modalities is generally complementary, with each modality contributing unique information to the status of a patient.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Steyaert et al., 2023
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“Advances in NLU have been crucial in improving AI's proficiency in interpreting and analyzing language across a spectrum of contexts, ranging from straightforward conversational exchanges to intricate textual data [190], [192], [193].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [192]
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Metadata
- Authors
- Steyaert, S., Pizurica, M., Nagaraj, D., Khandelwal, P., Hernandez-Boussard, T., Gentles, A. J., & Gevaert, O.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Corresponding author
- Steyaert, Sandra
- Gevaert, Olivier
- ORCID
- Venue
- Nature Machine Intelligence
- Published
- 2023-04-06
- Volume
- 5
- Issue
- 4
- Pages
- 351-362
- ISSN
- 2522-5839 (ISSN-L)
- PMC
- PMC10484010
- OpenAlex
- W4362673398
- Semantic Scholar
- 304b640c1b2559ac1442ac9be54853ac80ec248c
- Cited by
- 402
- References
- 159
- Citations by year
- 2026: 122 · 2025: 173 · 2024: 92 · 2023: 15
- FWCI
- 66.57
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Ghent University Academic Bibliography (Ghent University) (submittedVersion)
- Funders
- National Cancer Institute (R01CA260271)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- multimodal fusion, cancer biomarker discovery, deep learning, data sparsity, interpretability, electronic health records, digital pathology, radiographic images, molecular data, data standardization, personalized medicine, cancer diagnosis
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