Shen Shiying, Qi Wenhao, Liu Xin, Zeng Jianwen, Li Sixie, Zhu Xiaohong, Dong Chaoqun, Wang Bin, Shi Yankai, Yao Jiani, Wang Bingsheng, Jing Louxia, Cao Shihua, Liang Guanmian
School of Nursing, Hangzhou Normal University, No.2318, Yuhangtang Road, Yuhang District, Hangzhou, 310021, China.
Key Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou, China.
J Transl Med. 2025 Mar 19;23(1):348. doi: 10.1186/s12967-025-06371-z.
As global cancer incidence and mortality rise, digital twin technology in precision medicine offers new opportunities for cancer treatment.
This study aims to systematically analyze the current applications, research trends, and challenges of digital twin technology in tumor therapy, while exploring future directions.
Relevant literature up to 2024 was retrieved from PubMed, Web of Science, and other databases. Data visualization was performed using R and VOSviewer software. The analysis includes the research initiation and trends, funding models, global research distribution, sample size analysis, and data processing and artificial intelligence applications. Furthermore, the study investigates the specific applications and effectiveness of digital twin technology in tumor diagnosis, treatment decision-making, prognosis prediction, and personalized management.
Since 2020, research on digital twin technology in oncology has surged, with significant contributions from the United States, Germany, Switzerland, and China. Funding primarily comes from government agencies, particularly the National Institutes of Health in the U.S. Sample size analysis reveals that large-sample studies have greater clinical reliability, while small-sample studies emphasize technology validation. In data processing and artificial intelligence applications, the integration of medical imaging, multi-omics data, and AI algorithms is key. By combining multimodal data integration with dynamic modeling, the accuracy of digital twin models has been significantly improved. However, the integration of different data types still faces challenges related to tool interoperability and limited standardization. Specific applications of digital twin technology have shown significant advantages in diagnosis, treatment decision-making, prognosis prediction, and surgical planning.
Digital twin technology holds substantial promise in tumor therapy by optimizing personalized treatment plans through integrated multimodal data and dynamic modeling. However, the study is limited by factors such as language restrictions, potential selection bias, and the relatively small number of published studies in this emerging field, which may affect the comprehensiveness and generalizability of our findings. Moreover, issues related to data heterogeneity, technical integration, and data privacy and ethics continue to impede its broader clinical application. Future research should promote international collaboration, establish unified interdisciplinary standards, and strengthen ethical regulations to accelerate the clinical translation of digital twin technology in cancer treatment.
随着全球癌症发病率和死亡率的上升，精准医学中的数字孪生技术为癌症治疗提供了新的机遇。
本研究旨在系统分析数字孪生技术在肿瘤治疗中的当前应用、研究趋势和挑战，同时探索未来方向。
从PubMed、Web of Science和其他数据库中检索截至2024年的相关文献。使用R和VOSviewer软件进行数据可视化。分析内容包括研究起始和趋势、资助模式、全球研究分布、样本量分析以及数据处理和人工智能应用。此外，该研究还调查了数字孪生技术在肿瘤诊断、治疗决策、预后预测和个性化管理中的具体应用和有效性。
自2020年以来，肿瘤学领域对数字孪生技术的研究激增，美国、德国、瑞士和中国做出了重大贡献。资金主要来自政府机构，特别是美国国立卫生研究院。样本量分析表明，大样本研究具有更高的临床可靠性，而小样本研究则侧重于技术验证。在数据处理和人工智能应用方面，医学成像、多组学数据和人工智能算法的整合是关键。通过将多模态数据整合与动态建模相结合，数字孪生模型的准确性得到了显著提高。然而，不同数据类型的整合仍然面临与工具互操作性和标准化有限相关的挑战。数字孪生技术的具体应用在诊断、治疗决策、预后预测和手术规划方面显示出显著优势。
数字孪生技术通过整合多模态数据和动态建模优化个性化治疗方案，在肿瘤治疗中具有巨大潜力。然而，本研究受到语言限制、潜在选择偏倚以及该新兴领域已发表研究数量相对较少等因素的限制，这可能影响我们研究结果的全面性和普遍性。此外，数据异质性、技术整合以及数据隐私和伦理等问题继续阻碍其更广泛的临床应用。未来的研究应促进国际合作，建立统一的跨学科标准，并加强伦理规范，以加速数字孪生技术在癌症治疗中的临床转化。
