Machine Learning and Deep Learning Models in the Cancer Continuum
A Review of AI-based models in Early Detection, Diagnosis, and Treatment Planning
DOI:
https://doi.org/10.58445/rars.4098Keywords:
Machine Learning, Deep Learning, AI-based models, Early cancer detection, Outcome prediction, Treatment planning, Cancer diagnosisAbstract
Cancer is one of the leading causes of death worldwide, affecting people from all backgrounds. The need for early detection is essential, as it can prevent the spread of cancer before it takes over the body. However, cancer encompasses hundreds of classifications, making efficient diagnosis difficult. While new technology has been developed, it has trouble identifying abnormal cells, as cancer can resemble healthy cells. Recently, AI has become a ground-breaking tool within oncology, enabling early detection and characterization through advanced image analysis. Two major applications of AI are machine and deep learning models, which analyze data to make clinical predictions. These models support doctors and pathologists in diagnosis, prognosis, and therapy prediction for the patient. Furthermore, as time passes, AI can gather and record patient outcomes, improving the models’ results, as well as their detection and prediction techniques. With these newfound advancements, researchers have been forming AI-based models that could potentially save millions of lives. This study will review the innovative technologies of these AI models in oncology and how they are applied within the cancer continuum.
References
Ahmad, I., & Fahad Alqurashi. (2024). Early cancer detection using deep learning and medical imaging: A survey. Critical Reviews in Oncology/Hematology, 204(104528), 104528–104528. https://doi.org/10.1016/j.critrevonc.2024.104528
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2021). Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nature Reviews Clinical Oncology, 1–15. https://doi.org/10.1038/s41571-021-00560-7
Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., van der Laak, J., Hulsbergen-van de Kaa, C., & Litjens, G. (2020). Automated deep-learning system for gleason grading of prostate cancer using biopsies: A diagnostic study. The Lancet Oncology, 21(2), 233–241. https://doi.org/10.1016/s1470-2045(19)30739-9
Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018). Deep learning based tissue analysis predicts outcome in colo-rectal cancer. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-21758-3
Courtiol, P., Maussion, C., Moarii, M., Pronier, E., Pilcer, S., Sefta, M., Manceron, P., Toldo, S., Zaslavskiy, M., Le Stang, N., Girard, N., Elemento, O., Nicholson, A. G., Blay, J.-Y., Galateau-Sallé, F., Wainrib, G., & Clozel, T. (2019). Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nature Medicine, 25(10), 1519–1525. https://doi.org/10.1038/s41591-019-0583-3
Esteva, A., Feng, J., van der Wal, D., Huang, S.-C., Simko, J. P., DeVries, S., Chen, E., Schaeffer, E. M., Morgan, T. M., Sun, Y., Ghorbani, A., Naik, N., Nathawani, D., Socher, R., Michalski, J. M., Roach, M., Pisansky, T. M., Monson, J. M., Naz, F., … Mohamad, O. (2022). Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. Npj Digital Medicine, 5(1), 1–8. https://doi.org/10.1038/s41746-022-00613-w
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
Hausman, D. M. (2019). What is cancer? Perspectives in Biology and Medicine, 62(4), 778–784. https://doi.org/10.1353/pbm.2019.0046
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5
Kim, H.-E., Kim, H. H., Han, B.-K., Kim, K. H., Han, K., Nam, H., Lee, E. H., & Kim, E.-K. (2020). Changes in cancer detection and false-positive recall in mammography using artificial intelli-gence: A retrospective, multireader study. The Lancet Digital Health, 2(3), e138–e148. https://doi.org/10.1016/S2589-7500(20)30003-0
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Bio-technology Journal, 13(13), 8–17. https://doi.org/10.1016/j.csbj.2014.11.005
Kwong, G. A., Ghosh, S., Gamboa, L., Patriotis, C., Srivastava, S., & Bhatia, S. N. (2021). Synthetic biomarkers: A twenty-first century path to early cancer detection. Nature Reviews Cancer, 21(10), 655–668. https://doi.org/10.1038/s41568-021-00389-3
McIntosh, C., Conroy, L., Tjong, M. C., Craig, T., Bayley, A., Catton, C., Gospodarowicz, M., Helou, J., Isfahanian, N., Kong, V., Lam, T., Raman, S., Warde, P., Chung, P., Berlin, A., & Purdie, T. G. (2021). Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nature Medicine, 27(6), 999–1005. https://doi.org/10.1038/s41591-021-01359-w
McKinney, S. M. (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
Painuli, D., Bhardwaj, S., & köse, U. (2022). Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Computers in Biology and Medicine, 146, 105580. https://doi.org/10.1016/j.compbiomed.2022.105580
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