DEEP LEARNING APPLICATIONS IN ONCOLOGY FOR PERSONALIZED TREATMENT PLANNING
Keywords:
deep learning, oncology, personalized treatment, precision medicine, tumor characterization, therapy response prediction, outcome forecasting, medical imaging, genomics AI, multimodal datasets, cancer prognosis, therapy planning, clinical decision support, precision oncology, tumor heterogeneity, predictive analytics.Abstract
Personalized treatment planning in oncology requires precise prediction of tumor behavior and patient response to therapy. Deep learning (DL) techniques have emerged as powerful tools for analyzing complex, high-dimensional medical data, including imaging, genomic, and clinical records. This study explores the application of deep learning models for individualized cancer treatment, focusing on tumor characterization, therapy response prediction, and outcome forecasting. Models such as convolutional neural networks and recurrent neural networks were trained on multimodal datasets and validated for predictive accuracy. Results indicate that DL-based approaches outperform traditional methods, enabling more precise, patient-specific treatment strategies. Integration of explainable AI ensures interpretability and clinical trust.
References
Schwartz, R. et al. Deep learning in oncology: Transforming cancer diagnosis, prognosis, and treatment. Emerging Trends in Drugs, Addictions, and Health. 2025;100171. This review highlights the impact of deep learning models on oncology care, including personalized treatment planning by integrating complex data such as genomic and imaging information.
Kondrashova, O., Bradley, A., Pearson, J. V., et al. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Medicine. 2021;13:152. This article provides an overview of deep learning applications across oncology, focusing on diagnosis, treatment management, and the integration of diverse data types.
Ahmad, M. The impact of artificial intelligence on precision medicine and personalized oncology: A systematic review. Electronic Journal of General Medicine. 2025;22(6):em689. A recent narrative synthesis on how AI enhances precision treatment outcomes and customizes cancer care.
Artificial Intelligence in Cancer Research and Precision Medicine. PubMed. Reviews how AI, including deep learning, contributes to multiple oncology tasks from detection to treatment prediction.
Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions. Mayo Clinic Proceedings: Digital Health. 2025;100253. A comprehensive analysis of DL in oncology, addressing opportunities and barriers to clinical adoption.
Applications of artificial intelligence in non–small cell lung cancer: from precision diagnosis to personalized prognosis and therapy. J Transl Med. 2025. This article outlines AI advancements specifically in NSCLC treatment personalization.
Advancing personalized oncology: integration of AI in neoadjuvant treatment monitoring. PubMed. Systematic review on AI predicting treatment response in breast cancer, highlighting personalized approaches
Махсудов, В. Г. (2017). Гармоник тебранишларни инновацион технологиялар асосида ўрганиш («Кейс-стади»,«Ассесмент»,«Венн диаграммаси» мисолида). Современное образование (Узбекистан), (7), 11-16.
Maxsudov, V. G. (2018). Improvement of the methodological basics of training of the section «Mechanical oscillations» in higher educational institutions (Doctoral dissertation, Dissertation.–Tashkent: 2018. https://scholar. google. com/citations).
Zuparov, I. B., Ibragimova, M. N., Norbutayeva, M. K., Otaxonov, P. E., Normamatov, S. F., Safarov, U. Q., & Maxsudov, V. G. (2023). Modern directions and perspectives of using medical information systems. Switzerland: Innovations in technology and science education, 1218-1233.
Maxsudov, V. G., Ermetov, E. Y., & Jo, Z. R. rayeva. Types of physical education and the technologies of organization of matters in the modern education system. Fan, ta‘lim va amaliyot integratsiyasi 2022. Vol. 4. P29-34.
Maxsudov, V. G. (2018). Improvement of the methodological basics of training of the section «Mechanical oscillations» in higher educational institutions (Doctoral dissertation, Dissertation.–Tashkent).
Maxsudov, V. G. (2021). Technology of organization of modern lecture classes in higher education institutions. England: Modern views and research–2021, 160-166.
Valijon, M., & Fazliddin, A. (2025). ION CHANNELS: STRUCTURE AND THEIR ROLE IN CELLULAR FUNCTION. Web of Teachers: Inderscience Research, 3(5), 289-293.
