Digital twins (DTHs) and virtual twins (VTHs) in healthcare represent emerging technologies towards precision medicine, providing opportunities for patient-centric healthcare. Our scoping review aimed to map the current DTH and VTH technologies in oncology, summarize their technical solutions, and assess their credibility. A systematic search was conducted in the main bibliographic databases, identifying 441 records, of which 30 were included. The studies covered a wide range of cancers, including breast, lung, colorectal, and gastrointestinal malignancies, with DTH and VTH applications focusing on diagnosis, therapy, and monitoring. The results revealed heterogeneity in targeted topics, technical approaches, and outcomes. Most twining solutions use synthetic or limited real-world data, raising concerns regarding their reliability. Few studies have integrated real-time data and machine learning for predictive modeling. Technical challenges include data integration, scalability, and ethical considerations, such as data privacy and security. Moreover, the evidence lacks sufficient clinical validation, with only partial credibility in most cases. Our findings underscore the need for multidisciplinary collaboration among end-users and developers to address the technical and ethical challenges of DTH and VTH systems. Although promising for the future of personalized oncology, substantial steps are required to move beyond experimental frameworks and to achieve clinical implementation.
数字孪生与虚拟孪生技术作为精准医疗领域的新兴技术,为以患者为中心的医疗模式提供了发展契机。本研究通过范围综述方法,系统梳理肿瘤学领域数字孪生与虚拟孪生技术的应用现状,总结其技术实现方案并评估其可信度。我们在主要文献数据库中进行系统性检索,共获得441条记录,最终纳入30项研究。这些研究涵盖乳腺癌、肺癌、结直肠癌及胃肠道恶性肿瘤等多种癌症类型,数字孪生与虚拟孪生技术主要应用于诊断、治疗和监测环节。研究结果显示,现有技术在研究主题、技术路径及成果产出方面存在显著异质性。多数孪生解决方案采用合成数据或有限的真实世界数据,其可靠性值得商榷。仅有少数研究整合实时数据与机器学习技术进行预测建模。当前面临的技术挑战包括数据整合、系统可扩展性以及数据隐私安全等伦理问题。此外,现有证据普遍缺乏充分的临床验证,大多数案例仅具备部分可信度。本研究强调终端用户与开发者之间需要开展跨学科协作,共同应对数字孪生与虚拟孪生系统面临的技术与伦理挑战。尽管这些技术为个性化肿瘤学的未来发展带来希望,但要从实验框架走向临床实践仍需实现重大突破。
Advancing Precision Oncology with Digital and Virtual Twins: A Scoping Review