Machine learning in cancer biology: transforming diagnosis, prognosis, and treatment in modern medical research
Tufael1*, Md. Moyen Uddin PK2, 3
Journal of Ai ML DL 1 (1) 1-10 https://doi.org/10.25163/ai.1110405
Submitted: 30 June 2025 Revised: 16 September 2025 Accepted: 24 September 2025 Published: 24 September 2025
Abstract
Machine learning (ML) has emerged as a transformative tool in cancer biology, profoundly impacting diagnosis, prognosis, and treatment. ML algorithms make it possible to identify patterns and predictive markers in complex datasets from imaging, genomics, multiomics, and clinical records that are more accurate and efficient than those found with conventional techniques. In order to aid in early detection, tumour segmentation, and risk stratification, machine learning models have been applied to DNA sequences, mammograms, and histopathological images in the diagnosis of cancer. The integration of genetic, clinical, and tumor-specific data has improved prognosis, survival prediction, and recurrence risk models, enhancing patient stratification and guiding tailored interventions. Precision oncology has been made possible by ML-driven treatment applications that have expedited drug discovery, improved clinical trial design, and allowed for customised therapeutic approaches based on molecular tumour profiles. Significant obstacles still exist despite these developments, such as model interpretability, algorithmic bias, data privacy issues, and constraints in the healthcare system. For fair and successful clinical implementation, these problems must be resolved by strong regulatory frameworks, cooperative research projects, human-AI integration, and ongoing model evaluation. The ethical application of ML technologies in oncology, generalisability, and transparency are key future directions. With the potential to improve clinical decision-making, patient outcomes, and healthcare delivery disparities, machine learning (ML) has been positioned as a major force behind the shift towards data-driven, precision cancer care. The current status, difficulties, and exciting prospects of machine learning in contemporary cancer research and clinical practice are highlighted in this review.
Keywords: Machine learning, cancer diagnosis, cancer prognosis, precision oncology, artificial intelligence
References
Akter, L., Mondal, R. S., & Bhuiyan, M. N. A. (2025). Artificial Intelligence Application in Public Health: Advancement and Associated Challenges. Journal of Primeasia, 6(1), 1-10. https://doi.org/10.25163/primeasia.6110325
Amin, M. S., Rahman, A., & Rashid, M. J. (2025). AI in precision oncology: Revolutionizing early diagnosis, imaging and targeted therapies. Paradise, 1(1). https://doi.org/10.25163/paradise.1110340
Amin, M. S., & Rahman, A. (2025). Integrative approaches of AI in personalised disease management: From diagnosis to drug delivery. Paradise, 1(1). https://doi.org/10.25163/paradise.1110339
Agrawal, S., & Agrawal, J. (2015). Neural network techniques for cancer prediction: A survey. Procedia Computer Science, 60, 769–774. https://doi.org/10.1016/j.procs.2015.08.234
Ahmad, M., Khan, Z., Rahman, Z. U., Khattak, S. I., & Khan, Z. U. (2021). Can innovation shocks determine CO2 emissions (CO2e) in the OECD economies? A new perspective. Economics of Innovation and New Technology, 30(1), 89–109.
https://doi.org/10.1080/10438599.2019.1684643
Afolabi, L. O., Afolabi, M. O., Sani, M. M., et al. (2021). Exploiting the CRISPR-Cas9 gene-editing system for human cancers and immunotherapy. Clinical & Translational Immunology, 10(6), e1286. https://doi.org/10.1002/cti2.1286
Anderson, J. P., Parikh, J. R., Shenfeld, D. K., & Willke, R. J. (2016). Reverse engineering and evaluation of prediction models for progression to type 2 diabetes: An application of machine learning using electronic health records. Journal of Diabetes Science and Technology, 10(1), 6–18. https://doi.org/10.1177/1932296815620200
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021). Artificial intelligence in cancer research and precision medicine. Cancer Discovery, 11(4), 900–915. https://doi.org/10.1158/2159-8290.CD-21-0090
Bhuiyan, M. N. A., Mondal, R. S., & Akter, L. (2025). Advancing cancer imaging with artificial intelligence: Clinical application and challenges. Primeasia, 6(1), 1-11. https://doi.org/10.25163/primeasia.6110322
Cao, J.-S., Li, Z.-Y., Chen, M.-Y., et al. (2021). Artificial intelligence in gastroenterology and hepatology: Status and challenges. World Journal of Gastroenterology, 27(16), 1664. https://doi.org/10.3748/wjg.v27.i16.1664
Dananjayan, S., & Raj, G. M. (2020). Artificial intelligence during a pandemic: The COVID-19 example. International Journal of Health Planning and Management, 35(5), 1260–1262. https://doi.org/10.1002/hpm.2987
Enshaei, A., Robson, C., & Edmondson, R. (2015). Artificial intelligence systems as prognostic and predictive tools in ovarian cancer. Annals of Surgical Oncology, 22(12), 3970–3975. https://doi.org/10.1245/s10434-015-4475-6
Esteva, A., Kuprel, B., Novoa, R. A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Feng, H., Yang, B., Wang, J., et al. (2023). Identifying malignant breast ultrasound images using ViT-Patch. Applied Sciences, 13(6), 3489. https://doi.org/10.3390/app13063489
Gaur, K., & Jagtap, M. M. (2022). Role of artificial intelligence and machine learning in prediction, diagnosis, and prognosis of cancer. Cureus, 14(11), e31008. https://doi.org/10.7759/cureus.31008
Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., & Ranganath, R. (2019). Practical guidance on artificial intelligence for health-care data. The Lancet Digital Health, 1(4), e157–e159. https://doi.org/10.1016/S2589-7500(19)30084-6
Goldenberg, S. L., Nir, G., & Salcudean, S. E. (2019). A new era: Artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology, 16(7), 391–403. https://doi.org/10.1038/s41585-019-0193-3
Harbeck, N., & Gnant, M. (2017). Breast cancer. The Lancet, 389(10074), 1134–1150. https://doi.org/10.1016/S0140-6736(16)31891-8
Hart, G. R., Roffman, D. A., Decker, R., & Deng, J. (2018). A multi-parameterized artificial neural network for lung cancer risk prediction. PLoS ONE, 13(10), e0205264. https://doi.org/10.1371/journal.pone.0205264
Hollon, T. C., Pandian, B., Adapa, A. R., et al. (2020). Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nature Medicine, 26(1), 52–58. https://doi.org/10.1038/s41591-019-0715-9
Hu, F., Shi, X., Wang, H., et al. (2021). Is health contagious? Based on empirical evidence from China family panel studies’ data. Frontiers in Public Health, 9, 691746. https://doi.org/10.3389/fpubh.2021.691746
Huang, S., Yang, J., Fong, S., & Zhao, Q. (2020). Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Letters, 471, 61–71. https://doi.org/10.1016/j.canlet.2019.12.007
Iqbal, M. J., Javed, Z., Sadia, H., et al. (2021). Clinical applications of artificial intelligence and machine learning in cancer diagnosis: Looking into the future. Cancer Cell International, 21(1), 1–11. https://doi.org/10.1186/s12935-021-01981-1
Jianzhu, B., Shuang, L., Pengfei, M., Yi, Z., & Yanshu, Z. (2021). Research on early warning mechanism and model of liver cancer rehabilitation based on CS-SVM. Journal of Healthcare Engineering, 2021, 6658776. https://doi.org/10.1155/2021/6658776
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Jovel, J., & Greiner, R. (2021). An introduction to machine learning approaches for biomedical research. Frontiers in Medicine, 8, 689489. https://doi.org/10.3389/fmed.2021.689489
Kumar, Y., Gupta, S., Singla, R., & Hu, Y.-C. (2021). A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-021-09586-w
Liu, H., Liu, M., Li, D., Zheng, W., Yin, L., & Wang, R. (2022). Recent advances in pulse-coupled neural networks with applications in image processing. Electronics, 11(20), 3264. https://doi.org/10.3390/electronics11203264
McKinney, S. M., Sieniek, M., Godbole, V., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://doi.org/10.1038/s41586-019-1799-6
Mondal, R. S., Akter, L., & Bhuiyan, M. N. A. (2024). Artificial Intelligence in Drug Development and Delivery: Opportunities, Challenges, and Future Directions. Journal of Angiotherapy, 8(1), 1-10. https://doi.org/10.25163/angiotherapy.8810326
Mondal, R. S., Bhuiyan, M. N. A., & Akter, L. (2025). AI-driven innovations in cancer research and personalized healthcare. Journal of Angiotherapy, 9(1), 1-10. https://doi.org/10.25163/angiotherapy.9110321
Mondal, R. S., Bhuiyan, M. N. A., & Akter, L. (2024). Machine learning for chronic disease predictive analysis for early intervention and personalized care. Applied IT & Engineering, 2(1), 1-11. https://doi.org/10.25163/engineering.2110301
Mondal, R. S., Akter, L., & Bhuiyan, M. N. A. (2025). Integrating AI and ML techniques in modern microbiology. Applied IT & Engineering, 3(1), 1-10. https://doi.net/10.25163/engineering.3110323
Nartowt, B. J., Hart, G. R., Muhammad, W., Liang, Y., Stark, G. F., & Deng, J. (2020). Robust machine learning for colorectal cancer risk prediction and stratification. Frontiers in Big Data, 3, 6. https://doi.org/10.3389/fdata.2020.00006
Rana, M., Chandorkar, P., Dsouza, A., & Kazi, N. (2015). Breast cancer diagnosis and recurrence prediction using machine learning techniques. International Journal of Engineering Research and Technology, 4(4), 372–376. https://doi.org/10.15623/ijret.2015.0404066
Siddique, M. A. B., Debnath, A., Nath, N. D., Biswash, M. A. R., & Tufael. (2018). Advancing medical science through nanobiotechnology: Prospects, applications, and future directions. Journal of Primeasia, 1(1), 1–7. https://doi.org/10.25163/primeasia.1110163
Sidey-Gibbons, J. A. M., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: A practical introduction. BMC Medical Research Methodology, 19(1), 64. https://doi.org/10.1186/s12874-019-0681-4
Tufael, & Rahman Sunny, A. (2022). Transforming healthcare with artificial intelligence: Innovations, applications, and future challenges. Journal of Primeasia, 3(1), 1–6. https://doi.org/10.25163/primeasia.319802
Tufael, Rahman Sunny, A., et al. (2023). Artificial intelligence in addressing cost, efficiency, and access challenges in healthcare. Journal of Primeasia, 4(1), 1–5. https://doi.org/10.25163/primeasia.419798