Machine Learning in Precision Oncology: Integrating Multimodal Clinical Data for Treatment Selection
Machine Learning in Precision Oncology
DOI:
https://doi.org/10.64949/3dfqcq13Keywords:
Precision oncology, machine learning, multimodal data, treatment selection, radiogenomics, clinical decision supportAbstract
This article is a preprint and has not yet been peer-reviewed. Not for clinical use.
Integration of complex molecular, pathological, radiological, and longitudinal clinical data to guide treatment selection is becoming increasingly important for precision oncology. However, the volume and heterogeneity of these data often exceed the capacity of conventional clinical workflows and traditional statistical approaches. Machine learning, particularly deep learning, offers a computational framework for identifying non-linear patterns across high-dimensional datasets and generating clinically relevant predictions. This review summarises current and emerging applications of machine learning in precision oncology, with emphasis on multimodal data integration for biomarker discovery, patient stratification, treatment-response prediction, toxicity monitoring, and molecular tumour board support. Key examples include prediction of microsatellite instability from routine histology, integration of radiology, pathology, and genomic features to predict response to immune checkpoint inhibitors, multi-omic prediction of neoadjuvant chemotherapy response in breast cancer, and radiogenomic approaches for non-invasive assessment of tumour heterogeneity. Despite promising retrospective performance, many models remain investigational and require prospective, multi-centre validation before routine clinical implementation. Established reporting and evaluation frameworks, including TRIPOD+AI, DECIDE-AI, CONSORT-AI, SPIRIT-AI, and CLAIM, provide important guidance for translating machine-learning models from research settings into safe and effective clinical decision support. The central challenge is not whether machine learning can generate accurate predictions, but whether these predictions improve decisions that matter to patients.
References
1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-263. doi:10.3322/caac.21834 DOI: https://doi.org/10.3322/caac.21834
2. Hsu CY, Askar S, Alshkarchy SS, et al. AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions. Clin Exp Med. 2026;26(1). doi:10.1007/s10238-025-01965-9 DOI: https://doi.org/10.1007/s10238-025-01965-9
3. Marra A, Morganti S, Pareja F, et al. Artificial intelligence entering the pathology arena in oncology: current applications and future perspectives. Annals of Oncology. 2025;36(7):712-725. doi:10.1016/j.annonc.2025.03.006 DOI: https://doi.org/10.1016/j.annonc.2025.03.006
4. Fountzilas E, Pearce T, Baysal MA, Chakraborty A, Tsimberidou AM. Convergence of evolving artificial intelligence and machine learning techniques in precision oncology. NPJ Digit Med. 2025;8(1). doi:10.1038/s41746-025-01471-y DOI: https://doi.org/10.1038/s41746-025-01471-y
5. Tiwari A, Mishra S, Kuo TR. Current AI technologies in cancer diagnostics and treatment. Mol Cancer. 2025;24(1). doi:10.1186/s12943-025-02369-9 DOI: https://doi.org/10.1186/s12943-025-02369-9
6. Calderaro J, Seraphin TP, Luedde T, Simon TG. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma. J Hepatol. 2022;76(6):1348-1361. doi:10.1016/j.jhep.2022.01.014 DOI: https://doi.org/10.1016/j.jhep.2022.01.014
7. Yang H, Yang M, Chen J, Yao G, Zou Q, Jia L. Multimodal deep learning approaches for precision oncology: a comprehensive review. Brief Bioinform. 2025;26(1). doi:10.1093/bib/bbae699 DOI: https://doi.org/10.1093/bib/bbae699
8. Steyaert S, Pizurica M, Nagaraj D, et al. Multimodal data fusion for cancer biomarker discovery with deep learning. Nat Mach Intell. 2023;5(4):351-362. doi:10.1038/s42256-023-00633-5 DOI: https://doi.org/10.1038/s42256-023-00633-5
9. Jiang Z, Zhang H, Gao Y, Sun Y. Multi-omics strategies for biomarker discovery and application in personalized oncology. Molecular Biomedicine. 2025;6(1). doi:10.1186/s43556-025-00340-0 DOI: https://doi.org/10.1186/s43556-025-00340-0
10. Granata V, Fusco R, Setola SV, et al. An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models. Technol Cancer Res Treat. 2025;24. doi:10.1177/15330338251387928 DOI: https://doi.org/10.1177/15330338251387928
11. Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054-1056. doi:10.1038/s41591-019-0462-y DOI: https://doi.org/10.1038/s41591-019-0462-y
12. Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16(11):703-715. doi:10.1038/s41571-019-0252-y DOI: https://doi.org/10.1038/s41571-019-0252-y
13. Jha AK, Mithun S, Sherkhane UB, et al. Emerging role of quantitative imaging (radiomics) and artificial intelligence in precision oncology. Explor Target Antitumor Ther. 2023;4(4):569-582. doi:10.37349/etat.2023.00153 DOI: https://doi.org/10.37349/etat.2023.00153
14. He W, Huang W, Zhang L, Wu X, Zhang S, Zhang B. Radiogenomics: bridging the gap between imaging and genomics for precision oncology. MedComm (Beijing). 2024;5(9). doi:10.1002/mco2.722 DOI: https://doi.org/10.1002/mco2.722
15. Alum EU. AI-driven biomarker discovery: enhancing precision in cancer diagnosis and prognosis. Discover Oncology. 2025;16(1). doi:10.1007/s12672-025-02064-7 DOI: https://doi.org/10.1007/s12672-025-02064-7
16. Cao R, Yang F, Ma SC, et al. Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer. Theranostics. 2020;10(24):11080-11091. doi:10.7150/thno.49864 DOI: https://doi.org/10.7150/thno.49864
17. Bustos A, Payá A, Torrubia A, et al. XDEEP-MSI: Explainable bias-rejecting microsatellite instability deep learning system in colorectal cancer. Biomolecules. 2021;11(12). doi:10.3390/biom11121786 DOI: https://doi.org/10.3390/biom11121786
18. Shamai G, Livne A, Polónia A, et al. Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer. Nat Commun. 2022;13(1). doi:10.1038/s41467-022-34275-9 DOI: https://doi.org/10.1038/s41467-022-34275-9
19. Chen RJ, Lu MY, Williamson DFK, et al. Pan-cancer integrative histology-genomic analysis via multimodal deep learning. Cancer Cell. 2022;40(8):865-878.e6. doi:10.1016/j.ccell.2022.07.004 DOI: https://doi.org/10.1016/j.ccell.2022.07.004
20. Liao CY, Chen YM, Wu Y Te, et al. Personalized prediction of immunotherapy response in lung cancer patients using advanced radiomics and deep learning. Cancer Imaging. 2024;24(1). doi:10.1186/s40644-024-00779-4 DOI: https://doi.org/10.1186/s40644-024-00779-4
21. Li Y, Wu X, Fang D, Luo Y. Informing immunotherapy with multi-omics driven machine learning. NPJ Digit Med. 2024;7(1). doi:10.1038/s41746-024-01043-6 DOI: https://doi.org/10.1038/s41746-024-01043-6
22. Prelaj A, Miskovic V, Zanitti M, et al. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Annals of Oncology. 2024;35(1):29-65. doi:10.1016/j.annonc.2023.10.125 DOI: https://doi.org/10.1016/j.annonc.2023.10.125
23. Vanguri RS, Luo J, Aukerman AT, et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022;3(10):1151-1164. doi:10.1038/s43018-022-00416-8 DOI: https://doi.org/10.1038/s43018-022-00416-8
24. Sammut SJ, Crispin-Ortuzar M, Chin SF, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 2022;601(7894):623-629. doi:10.1038/s41586-021-04278-5 DOI: https://doi.org/10.1038/s41586-021-04278-5
25. Hoang DT, Dinstag G, Shulman ED, et al. A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics. Nat Cancer. 2024;5(9):1305-1317. doi:10.1038/s43018-024-00793-2 DOI: https://doi.org/10.1038/s43018-024-00793-2
26. Jiang Y, Zhou K, Sun Z, et al. Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell Rep Med. 2023;4(8). doi:10.1016/j.xcrm.2023.101146 DOI: https://doi.org/10.1016/j.xcrm.2023.101146
27. Caii W, Wu X, Guo K, Chen Y, Shi Y, Chen J. Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients. Cancer Immunology, Immunotherapy. 2024;73(8). doi:10.1007/s00262-024-03724-3 DOI: https://doi.org/10.1007/s00262-024-03724-3
28. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. Published online 2024. doi:10.1136/bmj-2023-078378 DOI: https://doi.org/10.1136/bmj-2023-078378
29. Rivera SC, Liu X, Chan AW, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI Extension. The BMJ. 2020;370. doi:10.1136/bmj.m3210 DOI: https://doi.org/10.1136/bmj.m3210
30. Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26(9):1364-1374. doi:10.1038/s41591-020-1034-x DOI: https://doi.org/10.1136/bmj.m3164
31. Vasey B, Nagendran M, Campbell B, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28(5):924-933. doi:10.1038/s41591-022-01772-9 DOI: https://doi.org/10.1038/s41591-022-01772-9
32. Mongan J, Moy L, Kahn CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers. Radiol Artif Intell. 2020;2(2). doi:10.1148/ryai.2020200029 DOI: https://doi.org/10.1148/ryai.2020200029
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