Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication
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Abstract
BACKGROUND: Deep learning models can infer cancer patient prognosis from molecular and anatomic pathology information. Recent studies that leveraged information from complementary multimodal data improved prognostication, further illustrating the potential utility of such methods. However, current approaches: 1) do not comprehensively leverage biological and histomorphological relationships and 2) make use of emerging strategies to "pretrain" models (i.e., train models on a slightly orthogonal dataset/modeling objective) which may aid prognostication by reducing the amount of information required for achieving optimal performance. In addition, model interpretation is crucial for facilitating the clinical adoption of deep learning methods by fostering practitioner understanding and trust in the technology. METHODS: Here, we develop an interpretable multimodal modeling framework that combines DNA methylation, gene expression, and histopathology (i.e., tissue slides) data, and we compare performance of crossmodal pretraining, contrastive learning, and transfer learning versus the standard procedure. RESULTS: Our models outperform the existing state-of-the-art method (average 11.54% C-index increase), and baseline clinically driven models (average 11.7% C-index increase). Model interpretations elucidate consideration of biologically meaningful factors in making prognosis predictions. DISCUSSION: Our results demonstrate that the selection of pretraining strategies is crucial for obtaining highly accurate prognostication models, even more so than devising an innovative model architecture, and further emphasize the all-important role of the tumor microenvironment on disease progression.
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Azher et al. (2023) developed an interpretable multimodal modeling framework combining DNA methylation, gene expression, and histopathology for prognostication of eight types of cancers.
“Azher et al. (2023) developed an interpretable multimodal modeling framework that combines DNA methylation, gene expression, and histopathology for the prognostication of eight types of cancers.”
From Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications (2025) · cites this paper as Azher et al., 2023
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1 retracted
Semaphorin 5A suppresses the proliferation and migration of lung adenocarcinoma cells
Ko, P., Lenka, G., Chen, Y., Chuang, E. Y.-Y., Tsai, M., Sher, Y., & Lai, L. (2019) · International Journal of Oncology · doi:10.3892/ijo.2019.4932 · Cited by 17 · see citing works
RetractedDeep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2022) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21708 · Cited by 17,196 · see citing works
Inductive Representation Learning on Large Graphs
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · arXiv · doi:10.48550/arxiv.1706.02216 · Cited by 10,639 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678 · see citing works
Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks
Silva, G., Rodrigues, M., Teixeira, A., et al. (2024) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.sea.2024.20 · Cited by 5,266 · see citing works
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data
Colaprico, A., Silva, T. C., Olsen, C., Garofano, L., Cava, C., Garolini, D., Sabedot, T. S., Malta, T. M., Pagnotta, S. M., Castiglioni, I., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1507 · Cited by 4,137 · see citing works
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338 · see citing works
Fast Kd-Trees for the Kullback-Leibler Divergence and Other Decomposable Bregman Divergences
Pham, T., & Wagner, H. (2025) · Leibniz international proceedings in informatics · doi:10.4230/lipics.wads.2025.45 · Cited by 2,353 · see citing works
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282 · see citing works
Targeting NF-κB pathway for the therapy of diseases: mechanism and clinical study
Yu, H., Lin, L., Zhang, Z., Zhang, H., & Hu, H. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00312-6 · Cited by 2,236 · see citing works
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203 · see citing works
The diverse roles of DNA methylation in mammalian development and disease
Greenberg, M., & Bourc’his, D. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0159-6 · Cited by 2,160 · see citing works
Cross-lingual Language Model Pretraining
Lample, G., & Conneau, A. (2019) · arXiv · doi:10.48550/arxiv.1901.07291 · Cited by 1,586 · see citing works
Representation Learning on Graphs with Jumping Knowledge NetworksPreprint
Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., & Jegelka, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03536 · Cited by 1,379 · see citing works
survminer: Drawing Survival Curves using 'ggplot2'
Kassambara, A., Kosinski, M., & Biecek, P. (2016) · doi:10.32614/cran.package.survminer · Cited by 1,218 · see citing works
Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027 · see citing works
Contrastive Representation Learning: A Framework and Review
Le-Khac, P. H., Healy, G., & Smeaton, A. F. (2020) · IEEE Access · doi:10.1109/access.2020.3031549 · Cited by 862 · see citing works
Artificial intelligence for multimodal data integration in oncology
Lipkova, J., Chen, R. J., Chen, B., Lu, M. Y., Barbieri, M., Shao, D., Vaidya, A. J., Chen, C., Zhuang, L., Williamson, D. F. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.09.012 · Cited by 777 · see citing works
Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3021387 · Cited by 729 · see citing works
Yang, G., Ye, Q., & Xia, J. (2022) · Information Fusion · doi:10.1016/j.inffus.2021.07.016 · Cited by 717 · see citing works
Multivariate Generalizations of the Wald-Wolfowitz and Smirnov Two-Sample Tests
Friedman, J. H., & Rafsky, L. C. (1979) · The Annals of Statistics · doi:10.1214/aos/1176344722 · Cited by 695 · see citing works
Pan-cancer integrative histology-genomic analysis via multimodal deep learning
Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.07.004 · Cited by 660 · see citing works
Captum: A unified and generic model interpretability library for PyTorch
Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Alsallakh, B., Reynolds, J., Melnikov, A., Kliushkina, N., Araya, C., Yan, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2009.07896 · Cited by 658 · see citing works
Mechanisms Linking Inflammation to Insulin Resistance
Chen, L., Chen, R., Wang, H., & Liang, F. (2015) · International Journal of Endocrinology · doi:10.1155/2015/508409 · Cited by 644 · see citing works
Hazard Ratio in Clinical Trials
Spruance, S. L., Reid, J., Grace, M., & Samore, M. H. (2004) · Antimicrobial Agents and Chemotherapy · doi:10.1128/aac.48.8.2787-2792.2004 · Cited by 547 · see citing works
Deep Graph InfomaxPreprint
Veličković, P., Fedus, W., Hamilton, W. L., Lió, P., Bengio, Y., & Hjelm, R. D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10341 · Cited by 537 · see citing works
Deep learning with multimodal representation for pancancer prognosis prediction
Cheerla, A., & Gevaert, O. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz342 · Cited by 421 · see citing works
Regulation of PD-L1 Expression by NF-κB in Cancer
Antonangeli, F., Natalini, A., Garassino, M. C., Sica, A., Santoni, A., & Rosa, F. D. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.584626 · Cited by 388 · see citing works
Molecular Genetics of Clear-Cell Renal Cell Carcinoma
Brugarolas, J. (2014) · Journal of Clinical Oncology · doi:10.1200/jco.2012.45.2003 · Cited by 309 · see citing works
Idos, G., Kwok, J., Bonthala, N. N., Kysh, L., Gruber, S. B., & Qu, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-60255-4 · Cited by 292 · see citing works
WSISA: Making Survival Prediction from Whole Slide Histopathological Images
Zhu, X.-L., Yao, J., Zhu, F.-Y., & Huang, J.-Z. (2017) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2017.725 · Cited by 286 · see citing works
Tabibu, S., Vinod, P. K. U., & Jawahar, C. V. (2019) · Scientific Reports · doi:10.1038/s41598-019-46718-3 · Cited by 253 · see citing works
Human Melanoma Progression in Skin Reconstructs
Meier, F. E., Nesbit, M., Hsu, M., Martin, B. G., Belle, P. A. V., Elder, D. E., Schaumburg‐Lever, G., Garbe, C., Walz, T. M., DONATIEN, P. D., et al. (2000) · American Journal Of Pathology · doi:10.1016/s0002-9440(10)64719-0 · Cited by 226 · see citing works
A Comprehensive Survey on Transfer LearningPreprint
Zhuang, F., Qi, Z., Duan, K., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02685 · Cited by 223 · see citing works
Roads to melanoma: Key pathways and emerging players in melanoma progression and oncogenic signaling
Paluncic, J., Kovačević, Ž., Jansson, P. J., Kalinowski, D. S., Merlot, A. M., Huang, M., Lok, H. C., Sahni, S., Lane, D. J. R., & Richardson, D. R. (2016) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2016.01.025 · Cited by 217 · see citing works
Prognostic models for breast cancer: a systematic review
Phung, M. T., Tin, S. T., & Elwood, M. (2019) · BMC Cancer · doi:10.1186/s12885-019-5442-6 · Cited by 214 · see citing works
Noorbakhsh, J., Farahmand, S., Foroughi pour, A., Namburi, S., Caruana, D., Rimm, D., Soltanieh-ha, M., Zarringhalam, K., & Chuang, J. H. (2020) · Nature Communications · doi:10.1038/s41467-020-20030-5 · Cited by 206 · see citing works
ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text DataPreprint
Qi, D., Su, L., Song, J. L., Cui, E., Bharti, T., & Sacheti, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.07966 · Cited by 201 · see citing works
Chen, R. J., Lu, M. Y., Shaban, M., Chen, C., Chen, T. Y., Williamson, D. F. K., & Mahmood, F. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-87237-3_33 · Cited by 176 · see citing works
The role of lysosomes in cancer development and progression
Tang, T., Yang, Z., Wang, D., Yang, X., Wang, J., Li, L., Wen, Q., Gao, L., Bian, X., & Yu, S. (2020) · Cell & Bioscience · doi:10.1186/s13578-020-00489-x · Cited by 169 · see citing works
Oshi, M., Takahashi, H., Tokumaru, Y., Yan, L., Rashid, O. M., Matsuyama, R., Endo, I., & Takabe, K. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21082921 · Cited by 157 · see citing works
Multimodal Machine Learning: A Survey and TaxonomyPreprint
Baltrušaitis, T., Ahuja, C., & Morency, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09406 · Cited by 152 · see citing works
Wnt/β-Catenin Signaling in Liver Cancers
Wang, W., Smits, R., Hao, H., & He, C. (2019) · Cancers · doi:10.3390/cancers11070926 · Cited by 143 · see citing works
Immune regulation of metastasis: mechanistic insights and therapeutic opportunities
Blomberg, O. S., Spagnuolo, L., & de Visser, K. E. (2018) · Disease Models & Mechanisms · doi:10.1242/dmm.036236 · Cited by 142 · see citing works
The remodelling of actin composition as a hallmark of cancer
Suresh, R., & Diaz, R. J. (2021) · Translational Oncology · doi:10.1016/j.tranon.2021.101051 · Cited by 140 · see citing works
Davis, J. N., Wojno, K. J., Daignault, S. D., Hofer, M. D., Kuefer, R., Rubin, M. A., & Day, M. L. (2006) · Cancer Research · doi:10.1158/0008-5472.can-06-2497 · Cited by 125 · see citing works
A modified DeepWalk method for link prediction in attributed social network
Berahmand, K., Nasiri, E., Rostami, M., & Forouzandeh, S. (2021) · Computing · doi:10.1007/s00607-021-00982-2 · Cited by 124 · see citing works
Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations
Huang, Z., Johnson, T. S., Han, Z., Helm, B. R., Cao, S., Zhang, C., Salama, P., Rizkalla, M., Yu, C. Y., Cheng, J., et al. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0686-1 · Cited by 99 · see citing works
Upregulation of MAPK pathway is associated with survival in castrate-resistant prostate cancer
Mukherjee, R., McGuinness, D. H., McCall, P. J., Underwood, M. A., Seywright, M. M., Orange, C., & Edwards, J. (2011) · British Journal of Cancer · doi:10.1038/bjc.2011.163 · Cited by 91 · see citing works
Chao, X., Qian, H., Wang, S., Fulte, S., & Ding, W. (2020) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2020.0169 · Cited by 89 · see citing works
An Empirical Study of Graph Contrastive LearningPreprint
Zhu, Y., Xu, Y., Liu, Q., & Wu, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.01116 · Cited by 89 · see citing works
Hao, J., Kim, Y., Mallavarapu, T., Oh, J. H., & Kang, M. (2019) · BMC Medical Genomics · doi:10.1186/s12920-019-0624-2 · Cited by 82 · see citing works
Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural NetworksPreprint
Levy, J. J., Haudenschild, C., Barwick, C., Christensen, B., & Vaickus, L. (2020) · bioRxiv · doi:10.1101/2020.08.01.231639 · Cited by 71 · see citing works
Self-citationTopological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks
Levy, J., Haudenschild, C., Barwick, C., Christensen, B. C., & Vaickus, L. J. (2020) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811232701_0027 · Cited by 55 · see citing works
Smooth muscle differentiation and patterning in the urinary bladder
Tasian, G. E., Cunha, G. R., & Baskin, L. S. (2010) · Differentiation · doi:10.1016/j.diff.2010.05.004 · Cited by 47 · see citing works
Comparing nonnested Cox models
Fine, J. P. (2002) · Biometrika · doi:10.1093/biomet/89.3.635 · Cited by 47 · see citing works
Zhang, Z., Wiencke, J. K., Kelsey, K. T., Koestler, D. C., Christensen, B. C., & Salas, L. A. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03736-6 · Cited by 40 · see citing works
Wang, S., Zhang, H., Liu, Z., & Liu, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.800853 · Cited by 39 · see citing works
Ramp, U., Mahotka, C., Heikaus, S., Shibata, T., Grimm, M., Willers, R., & Gabbert, H. E. (2007) · PubMed · doi:10.14670/hh-22.1099 · Cited by 38 · see citing works
Involvement of the Androgen and Glucocorticoid Receptors in Bladder Cancer
McBeth, L., Grabnar, M., Selman, S. H., & Hinds, T. D. (2015) · International Journal of Endocrinology · doi:10.1155/2015/384860 · Cited by 35 · see citing works
Levy, J., Chen, Y., Azizgolshani, N., Petersen, C. L., Titus, A., Moen, E. L., Vaickus, L. J., Salas, L. A., & Christensen, B. C. (2021) · npj Systems Biology and Applications · doi:10.1038/s41540-021-00193-7 · Cited by 25 · see citing works
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations
Li, H., Ding, W., Kang, Y., Liu, T., Wu, Z., & Liu, Z. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.323 · Cited by 25 · see citing works
Levy, J., Salas, L. A., Christensen, B. C., Sriharan, A., & Vaickus, L. J. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0036 · Cited by 24 · see citing works
Ahn, S., Chung, Y. R., Seo, A. N., Kim, M., Woo, J. W., & Park, S. Y. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233037 · Cited by 22 · see citing works
Regulated lytic cell death in breast cancer
Liu, M., Wang, L., Xia, X., Wu, Y., Zhu, C., Duan, M., Wei, X., Hu, J., & Lei, L. (2021) · Cell Biology International · doi:10.1002/cbin.11705 · Cited by 19 · see citing works
Cytokines secreted by inflamed oral mucosa: implications for oral cancer progression
Danella, E. B., de Medeiros, M. C., & D’Silva, N. J. (2023) · Oncogene · doi:10.1038/s41388-023-02649-y · Cited by 16 · see citing works
Azher, Z., Vaickus, L. J., Salas, L. A., Christensen, B. C., & Levy, J. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507032 · Cited by 15 · see citing works
Finetuning Pretrained Transformers into Variational Autoencoders
Park, S., & Lee, J. (2021) · Workshop on Insights from Negative Results in NLP (Insights) · doi:10.18653/v1/2021.insights-1.5 · Cited by 13 · see citing works
PAN-CANCER INTEGRATIVE HISTOLOGY-GENOMIC ANALYSIS VIA INTERPRETABLE MULTIMODAL DEEP LEARNING
Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T., Lipková, J., Shaban, M., Shady, M., Williams, M., Joo, B., Noor, Z., et al. (2022) · Journal of Pathology Informatics · doi:10.1016/j.jpi.2022.100057 · Cited by 12 · see citing works
Shallow Sparsely-Connected Autoencoders for Gene Set Projection
Gold, M. P., LeNail, A., & Fraenkel, E. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0034 · Cited by 11 · see citing works
Chen, R. J., Lu, M. Y., Wang, J., Williamson, D. F. K., Rodig, S. J., Lindeman, N. I., & Mahmood, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.08937 · Cited by 9 · see citing works
Classification and authentication of one-dimensional behavioral biometrics
Monaco, J. V. (2014) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2014.6996253 · Cited by 8 · see citing works
Cancer-associated keratinocytes: new members of the microenvironment in head and neck cancer
Danella, E. B., de Medeiros, M. C., & D’Silva, N. J. (2021) · Molecular & Cellular Oncology · doi:10.1080/23723556.2021.1933329 · Cited by 7 · see citing works
Fan, X., Wen, J., Bao, L., Gao, F., Li, Y., & He, D. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.681809 · Cited by 7 · see citing works
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsPreprint
Li, H., Kang, Y., Liu, T., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.00181 · Cited by 4 · see citing works
Finetuning Pretrained Transformers into Variational AutoencodersPreprint
Park, S., & Lee, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02446 · Cited by 3 · see citing works
Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep LearningPreprint
Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T., Lipková, J., Shaban, M., Shady, M., Williams, M., Joo, B., Noor, Z., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02278 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [73]Benkirane H, Vakalopoulou M, Christodoulidis S, Garberis I-J, Michiels S, Cournède P-H. Hyper-AdaC: Adaptive clustering-based hypergraph representation of whole slide images for survival analysis. In: Proceedings of the 2nd Machine Learning for Health symposium. PMLR; 2022. p. 405–18.
Cited by
Sorted by citations
Schouten, D., Nicoletti, G., Dille, B., Chia, C., Vendittelli, P., Schuurmans, M., Litjens, G., & Khalili, N. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103621 · Cited by 108 · see citing works
Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends
Gogoshin, G., & Rodin, A. S. (2023) · Cancers · doi:10.3390/cancers15245858 · Cited by 64 · see citing works
“Azher et al. [52] compared different pretraining strategies for multimodal (methylation, expression, histopathology) GNN-based cancer prognostication and concluded that appropriate pretraining strategies might be more important than innovations in model architectures for highly accurate prediction.”
BackgroundProgress and opportunities of foundation models in bioinformatics
Li, Q., Hu, Z., Wang, Y., Li, L., Fan, Y., King, I., Jia, G., Wang, S., Song, L., & Li, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae548 · Cited by 62 · see citing works
“Remarkably, the choice of pretraining strategies holds significant importance in attaining optimal performance [39].”
BackgroundApplication of artificial intelligence in cancer diagnosis and tumor nanomedicine
Wang, J., Liu, G., Zhou, C. P., Cui, X., Wang, W., Wang, J., Huang, Y., Jiang, J., Wang, Z., Tang, Z., et al. (2024) · Nanoscale · doi:10.1039/d4nr01832j · Cited by 33 · see citing works
Federated and transfer learning for cancer detection based on image analysis
Bechar, A., Medjoudj, R., Elmir, Y., Himeur, Y., & Amira, A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10956-y · Cited by 36 · see citing works
“In the field of brain image analysis, multimodal deep TL techniques have been proposed to utilize different viewing planes (axial, sagittal and coronal) in MRI brain images [309].”
Napravnik, M., Hržić, F., Tschauner, S., & Štajduhar, I. (2024) · BioData Mining · doi:10.1186/s13040-024-00373-1 · Cited by 12 · see citing works
“However, when considering medical ML image processing, there are studies suggesting that medical radiology datasets serve as more suitable sources for training pretrained models in comparison to natural image datasets like Ima-geNet [11–13].”
BackgroundInsights to aging prediction with AI based epigenetic clocks
Levy, J., Diallo, A., Montivero, M. K. S., Gabbita, S., Salas, L. A., & Christensen, B. C. (2024) · Epigenomics · doi:10.1080/17501911.2024.2432854 · Cited by 11 · see citing works
Multi-omics driven computational framework for cancer molecular subtype classification
Abbasi, A. F., Sajjad, M., Asim, M. N., Vollmer, S., & Dengel, A. R. (2025) · Scientific Reports · doi:10.1038/s41598-025-32051-5 · Cited by 6 · see citing works
Azher, Z., Fatemi, M., Lu, Y., Srinivasan, G., Diallo, A., Christensen, B. C., Salas, L. A., Kolling, F., Perreard, L., Palisoul, S. M., et al. (2023) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811286421_0036 · Cited by 6 · see citing works
Lu, Y., Srinivasan, G., Preum, S. M., Pettus, J. R., Davis, M., Greenburg, J., Vaickus, L. J., & Levy, J. (2023) · medRxiv · doi:10.1101/2023.09.10.23295318 · Cited by 5 · see citing works
Xie, T., Xue, H., Huang, A., Yan, H., & Yuan, J. (2025) · Pathology - Research and Practice · doi:10.1016/j.prp.2025.156035 · Cited by 4 · see citing works
Flannery, B. T., DeSilvio, T., Hariri, M., Sadri, A. R., Heller, N., Weight, C. J., & Viswanath, S. E. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111603 · Cited by 3 · see citing works
“Similar observations regarding the importance of model initialization have recently been reported in the context of pretraining approaches in medical imaging [5,30] and multimodal/multi-omic deep learning survival methods [6,52].”
McLoughlin, A., YinHo, H., Zhao, X., Hakansson, A. K., Moradi, A., Xu, Q. J., & Liu, Y. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00097 · Cited by 2 · see citing works
Zhang, H., Huang, Q., Shang, B., Su, G., & Tu, H. K. (2026) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-014429 · Cited by 2 · see citing works
“120 121 These strategies are relatively interpretable and highlight the contribution of TME components such as TILs and fibroblasts in predicting treatment responses and mutational status; however, their heavy reliance on expert annotations limits clinical applicability.”
Zhu, H., Wang, G., Liao, Z., & Zhang, W. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113473 · Cited by 1 · see citing works
Nguyen, T., Panwar, V., Jarmale, V. N., Perny, A., Dusek, C., Cai, Q., Kapur, P., Danuser, G. M., & Rajaram, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.18.643999 · Cited by 1 · see citing works
Meng, F., Zhang, H., Yan, R., Chuai, G., Li, C., & Liu, Q. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72384-1_20
FROM CANCER MOLECULAR SUBTYPE TO AI HYPE: BENCHMARKING AI IN CANCER MOLECULAR SUBTYPINGPreprint
Abbasi, A. F., Sajjad, M., Asim, M. N., Vollmer, S. J., & Dengel, A. R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.10.642355
“In terms of datasets, TCGA has been the most frequently utilized resource in over 30 studies for multi-omics data [32, 33, 34, 25].”
Learning multimodal explainable AI models from medical images and tabular data: proof of concept
Malafaia, M., Schlender, T., Bosman, P. A. N., & Alderliesten, T. (2025) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3040402
“10,11 However, these works rely on post-hoc explainability restricted to final predictions, without addressing the feature extraction process for each modality or enabling integrated interpretability across modalities.”
Explainable AI for multi-omics in precision medicine: a systematic review
Hasnain, M., Hameed, S., Azim, K., & Waseem, M. (2026) · Exploration of Digital Health Technologies · doi:10.37349/edht.2026.1011102
- Authors
- Azher, Z. L., Suvarna, A., Chen, J.-Q., Zhang, Z., Christensen, B. C., Salas, L. A., Vaickus, L. J., & Levy, J. J.
- Affiliations
- Dartmouth College (US)
- Dartmouth–Hitchcock Medical Center (US)
- Corresponding author
- Levy, Joshua J.
- ORCID
- Venue
- BioData Mining
- Published
- 2023-07-22
- Volume
- 16
- Issue
- 1
- Pages
- 23-23
- ISSN
- 1756-0381 (ISSN-L)
- PMC
- PMC10363299
- OpenAlex
- W4385147142
- Cited by
- 20
- References
- 76
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 5 · 2023: 3
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BioData Mining (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (P20GM104416)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- multimodal deep learning, contrastive learning, transfer learning, DNA methylation, gene expression, histopathology, interpretable models, tumor microenvironment, model interpretability
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