Multimodal data fusion for cancer biomarker discovery with deep learning

Steyaert, Sandra; Pizurica, Marija; Nagaraj, Divya; Khandelwal, Priya; Hernandez-Boussard, Tina; Gentles, Andrew J.; Gevaert, Olivier

doi:10.1038/s42256-023-00633-5

Cited by 402Radiomics and Machine Learning in Medical ImagingMachine Learning in HealthcareAI in cancer detection

Multimodal data fusion for cancer biomarker discovery with deep learning

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Steyaert, Sandra, Pizurica, Marija, Nagaraj, Divya, Khandelwal, Priya, Hernandez-Boussard, Tina, Gentles, Andrew J., Gevaert, Olivier·2023·Nature Machine Intelligence

DOI 10.1038/s42256-023-00633-5PMC PMC10484010

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Abstract

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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AI systems can profit significantly from assimilating multimodal data into prediction and classification models to imitate integrative human clinical decision-making.
“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]
The Introduction (page not provided) says combining data modalities can make healthcare AI more sensitive and robust for diagnosis, prognosis, and treatment assignment. The section on multimodal data fusion in oncology also calls for integrating those data into clinical decision systems. The paper does not say that these models imitate human clinical decision-making, and its discussion is specifically about oncology rather than AI systems generally. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Assimilating multimodal data can boost AI systems' robustness and accuracy, enable discovery of new biomarkers and therapeutic targets, and improve model performance.
“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]
The Introduction (page unavailable) says combining modalities can make diagnosis, prognosis and treatment assignment more robust and provide a resource for biomarker discovery. It does not establish discovery of new therapeutic targets. The model-performance clause is attached to separate citations (15–17), so it is not assessed against this source. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Understanding 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.
“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]
The Introduction says that identifying complex genetic interactions can enhance understanding of tumour biology. The sections on multimodal biomarkers and future directions also discuss biomarker discovery and associations between modalities. But the paper frames these as potential avenues in oncology; it does not establish that understanding cross-modality regulatory networks is essential to explaining disease development or identifying new drug targets. (LLM, conf=0.85)
Experimental — accuracy not yet measured
High costs of biomedical data generation, difficulties in patient follow-up, and privacy restrictions limit size of training datasets and lead to missing data modalities.
“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].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [28]
In “Current challenges and future directions for multimodal data fusion” (page not available), the paper says cost can leave patients without some data modalities, while privacy concerns and annotation labour limit large matched, labelled cohorts. It also notes that cohorts often favour patients with continuous access to care, but does not identify difficulties in patient follow-up as a cause of small training datasets or missing modalities. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Data generated by diverse medical modalities is generally complementary, with each modality contributing unique information to the status of a patient.
“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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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: Steyaert, Sandra
Pizurica, Marija
Khandelwal, Priya
Hernandez-Boussard, Tina
Gentles, Andrew J.
Gevaert, Olivier
Venue: Nature Machine Intelligence
Publisher: Springer Science and Business Media LLC
Published: 2023-04-06
Volume: 5
Issue: 4
Pages: 351-362
ISSN: 2522-5839 (ISSN-L)
DOI: 10.1038/s42256-023-00633-5
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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