<![CDATA[because substantial genetic heterogeneity, dynamic tumor evolution, and variable treatment responses limit the effectiveness of standardized clinical protocols. 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. Findings demonstrated that Digital Twin-assisted treatment planning significantly improved simulation accuracy, treatment response prediction, therapeutic precision, physician decision confidence, and multidisciplinary coordination while reducing anticipated adverse treatment events. 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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