From Simulation to Treatment: Advancing Tumor Therapy with Digital Twin Technology

Abstract

The incidence and mortality of cancer continue to rise globally, and digital twin technology brings new opportunities for precision cancer treatment. To systematically analyze the current status, research trends, major challenges and future directions of the application of this technology in tumor therapy. We searched PubMed, Web of Science and other databases for relevant literature up to 2024, and used R language and VOSviewer software to visualize the data. The analysis covered research initiation and trend, funding mode, global research distribution, sample size, data processing and artificial intelligence application, and also examined the specific application and effectiveness of this technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management. From 2020 onwards, there has been a surge of research in digital twin technology in the field of oncology, concentrated in the United States, Germany, Switzerland, and China. Government agencies, especially the National Institutes of Health (NIH), funded the majority. Sample size analysis shows that large samples are highly reliable for clinical applications, while small samples focus on technology validation. On the application of data processing and artificial intelligence, the algorithm for combining medical imaging and multi-omics data is the key, and the integration of multi-modal data and dynamic modeling improve the accuracy of the model, but the integration of different data types is limited by the interoperability of the tools and the degree of standardization. In specific applications, the technology has obvious advantages in diagnosis, treatment decision-making, prognosis prediction, and surgery planning. Digital twin technology has great potential in oncology treatment, lending multimodal data integration and dynamic modeling to optimize personalized treatment plans. However, there are data diversity, sample size imbalance, technical integration difficulties, data privacy and ethical issues. In the future, it is necessary to promote the widespread application of digital twin technology in the field of precision oncology through international cooperation, building a data sharing platform, unifying interdisciplinary standards, and strengthening ethical regulations, so as to improve the treatment effect and patients’ quality of life.

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