Personalized medicine is based on the principle that each patient's treatment should be designed according to their unique characteristics rather than universal protocols. This approach is particularly critical in oncology, where the disease is complex, dynamic, and adaptive. This policy brief is developed based on research aimed at modeling the interaction between drugs and cancer cells using Game Theory and Stackelberg competition. The findings demonstrated that the Stackelberg model is capable of predicting three patterns of drug resistance: rapid, gradual, and cross-resistance. By determining optimal dosing strategies including a strong initial dose, gradual adaptation, and rest periods the model can increase patient survival by up to 23% and reduce side effects by 31%. Based on these findings and expert consultation, three policy options were presented: "development and implementation of the Stackelberg model in selected cancer centers," "maintaining the status quo," and "developing simple statistical models based on retrospective data." Analysis of these options showed that, given current limitations in data infrastructure, specialized human resources, and budget, the first priority should be the development of simple statistical models and the standardization of clinical data collection. This action can institutionalize a culture of data-driven decision-making within the medical community and pave the way for adopting more advanced models, such as Stackelberg, in subsequent phases. Simultaneously, establishing a national consortium for genomic and clinical cancer data, allocating specialized research funds for multidisciplinary projects, and designing clinical trials to validate these models are essential steps to facilitate the implementation of mathematical-modeling-based personalized medicine.