Digital Twins for Radiation Oncology
Digital Twins for Radiation Oncology
复制标题
放射肿瘤学数字孪生
DOI：
10.1145/3543873.3587688
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Deng, Jun
中科院分区：
文献类型：
--
作者：
Jensen, James;Deng, Jun
数字双胞胎技术已经彻底改变了许多行业的最先进实践，数字双胞胎在建模癌症患者方面有着天然的应用。通过在比传统机器学习模型更基础的层面上模拟患者，数字双胞胎可以通过预测每个患者的结果轨迹来提供独特的见解。这有许多相关的好处，包括患者特定的临床决策支持和大规模虚拟临床试验的潜力。从历史上看，使用数字孪生技术对癌症患者进行建模是不可行的，因为影响每个患者结果轨迹的变量很多，包括基因型、表型、社会和环境因素。然而，由于最近的进展，放射肿瘤学中的数字双胞胎之路正在成为可能，例如估计患者特定细胞，分子和组织学分布的多尺度建模技术，以及能够在多个机构中安全有效地集中患者数据的现代加密技术。随着这些和其他未来的科学进步，用于放射肿瘤学的数字双胞胎可能变得可行。这项工作讨论了患者特定数字孪生和数字孪生网络的可能通用架构，以及数字孪生技术在放射肿瘤学中应用的好处、现有障碍和潜在途径。
Digital twin technology has revolutionized the state-of-the-art practice in many industries, and digital twins have a natural application to modeling cancer patients. By simulating patients at a more fundamental level than conventional machine learning models, digital twins can provide unique insights by predicting each patient's outcome trajectory. This has numerous associated benefits, including patient-specific clinical decision-making support and the potential for large-scale virtual clinical trials. Historically, it has not been feasible to use digital twin technology to model cancer patients because of the large number of variables that impact each patient's outcome trajectory, including genotypic, phenotypic, social, and environmental factors. However, the path to digital twins in radiation oncology is becoming possible due to recent progress, such as multiscale modeling techniques that estimate patient-specific cellular, molecular, and histological distributions, and modern cryptographic techniques that enable secure and efficient centralization of patient data across multiple institutions. With these and other future scientific advances, digital twins for radiation oncology will likely become feasible. This work discusses the likely generalized architecture of patient-specific digital twins and digital twin networks, as well as the benefits, existing barriers, and potential gateways to the application of digital twin technology in radiation oncology.
DOI：
10.1007/s11831-020-09405-5
发表时间：
2021-05
期刊：
Archives of computational methods in engineering : state of the art reviews
影响因子：
--
作者：
Peng GCY;Alber M;Tepole AB;Cannon WR;De S;Dura-Bernal S;Garikipati K;Karniadakis G;Lytton WW;Perdikaris P;Petzold L;Kuhl E
通讯作者：
Kuhl E