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Cited by 8Explainable Artificial Intelligence (XAI)Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and Biomarkers

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Explainable artificial intelligence for multi-modal cancer analysis: From genomics to immunology

Zhang, Min, Yin, Lina·2026·Critical Reviews in Oncology/Hematology
DOI 10.1016/j.critrevonc.2025.105040
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Abstract

Cancer remains one of the most biologically intricate and heterogeneous diseases, with variability spanning genetic, epigenetic, metabolic, spatial, and immunological dimensions. This multifactorial complexity is shaped by tumor-microenvironment interactions, immune modulation, clonal evolution, and therapeutic pressures, creating profound challenges for accurate prognosis, treatment selection, and durable response prediction. Conventional single-modality analytical approaches often capture only isolated features of this landscape, limiting their ability to generalize across patient populations and clinical settings. Selective multimodal deep learning (MDL) provides a transformative framework by mechanism-informed integration of complementary biomedical data-including genomics, transcriptomics, histopathology, medical imaging, proteomics, metabolomics, electronic health records (EHRs), and immunological profiles into unified models capable of capturing cross-scale dependencies and emergent patterns, while mitigating interference through targeted fusion strategies. The interpretability of these models is enhanced by explainable artificial intelligence (XAI), which translates complex computational predictions into transparent, biologically grounded explanations that align with known oncological and immunological processes. A distinctive focus of this review is immunology-informed integration, which connects predictive signatures to tumor-immune microenvironment dynamics, enhanc…

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Metadata

Authors
Zhang, M., & Yin, L.
Affiliations
Shandong Management University (CN)
Corresponding author
Zhang, Min
ORCID
Zhang, Min
Venue
Critical Reviews in Oncology/Hematology
Publisher
Elsevier BV
Published
2026-03
Volume
219
Pages
105040
ISSN
1040-8428 (ISSN-L)
DOI
10.1016/j.critrevonc.2025.105040
OpenAlex
W4416556358
Cited by
5
References
202
Citations by year
2026: 5
FWCI
0.48
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Critical Reviews in Oncology/Hematology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Keywords
explainable AI, multimodal deep learning, cancer, tumor immune microenvironment, multimodal fusion, precision oncology, biomarker discovery, data harmonization, modality imbalance, federated learning, causal inference, model interpretability
MeSH terms
Deep Learning, Artificial Intelligence, Humans, Immunotherapy, Neoplasms, Biomarkers, Tumor, Genomics, Precision Medicine, Tumor Microenvironment

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