<![CDATA[Cancer treatment increasingly requires individualized therapeutic strategies because substantial genetic heterogeneity, dynamic tumor evolution, and variable treatment responses limit the effectiveness of standardized clinical protocols. Digital Twin technology has emerged as a promising computational paradigm capable of integrating genomic, physiological, imaging, and clinical data into patient-specific virtual models that support personalized therapeutic planning. This study aimed to evaluate the implementation of Digital Twins for cancer therapy simulation and to examine their contribution to genetic-based medical personalization and clinical decision support. A mixed-methods sequential explanatory design was employed involving 480 patients treated at tertiary oncology centers. Quantitative analyses included descriptive statistics, structural equation modeling, survival analysis, mediation and moderation analyses, whereas qualitative evidence was obtained through semi-structured interviews, clinical observations, multidisciplinary case reviews, and document analysis. Results indicate that Digital Twin technology provides a comprehensive precision oncology framework by integrating computational intelligence, genomic medicine, and real-time clinical information to optimize individualized cancer therapy, improve evidence-based decision-making, and enhance long-term patient outcomes while supporting the broader implementation of personalized medicine. ]]>
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