Ștefănigă Sebastian Aurelian, Cordoș Ariana Anamaria, Ivascu Todor, Feier Catalin Vladut Ionut, Muntean Călin, Stupinean Ciprian Viorel, Călinici Tudor, Aluaș Maria, Bolboacă Sorana D
Department of Computer Science, West University of Timișoara, Vasile Pârvan Blvd., No. 4, 300223 Timișoara, Romania.
Department of Surgery-Practical Abilities, "Iuliu Hațieganu" University of Medicine and Pharmacy, Marinescu Street, No. 23, 400337 Cluj-Napoca, Romania.
Cancers (Basel). 2024 Nov 13;16(22):3817. doi: 10.3390/cancers16223817.
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.
医疗保健领域的数字孪生（DTH）和虚拟孪生（VTH）代表了迈向精准医学的新兴技术，为以患者为中心的医疗保健提供了机遇。我们的范围综述旨在梳理肿瘤学领域当前的DTH和VTH技术，总结其技术解决方案，并评估其可信度。我们在主要的文献数据库中进行了系统检索，共识别出441条记录，其中30条被纳入。这些研究涵盖了广泛的癌症类型，包括乳腺癌、肺癌、结直肠癌和胃肠道恶性肿瘤，DTH和VTH的应用集中在诊断、治疗和监测方面。结果显示，在目标主题、技术方法和成果方面存在异质性。大多数孪生解决方案使用合成数据或有限的真实世界数据，这引发了对其可靠性的担忧。很少有研究将实时数据和机器学习整合用于预测建模。技术挑战包括数据整合、可扩展性以及伦理考量，如数据隐私和安全。此外，现有证据缺乏充分的临床验证，在大多数情况下可信度仅为部分可信。我们的研究结果强调，终端用户和开发者之间需要开展多学科合作，以应对DTH和VTH系统的技术和伦理挑战。尽管对个性化肿瘤学的未来很有前景，但仍需要采取实质性步骤，以超越实验框架并实现临床应用。
