放射治疗的目的是实现高肿瘤控制概率，同时尽量减少对正常组织的损害。因此，针对个体患者的个性化放射治疗取决于将物理治疗计划与肿瘤控制和正常组织并发症的预测模型相结合。可以使用各种丰富的数据源来改进预测模型，包括肿瘤和正常组织基因组学、放射组学和剂量组学。深度学习将推动正常组织耐受性分类、预测治疗中肿瘤变化、跟踪累积剂量分布以及基于成像量化肿瘤对放疗的反应等方面的改进。针对特定患者的机械计算机模拟（“数字双胞胎”）也可用于指导适应性放射治疗。总体而言，我们正在进入一个时代，改进的建模方法将允许使用新的可用数据源来更好地指导放射治疗。版权所有 © 2024。由 Elsevier Inc. 出版。

Radiotherapy aims to achieve a high tumor control probability while minimizing damage to normal tissues. Personalizing radiotherapy treatments for individual patients, therefore, depends on integrating physical treatment planning with predictive models of tumor control and normal tissue complications. Predictive models could be improved using a wide range of rich data sources, including tumor and normal tissue genomics, radiomics, and dosiomics. Deep learning will drive improvements in classifying normal tissue tolerance, predicting intra-treatment tumor changes, tracking accumulated dose distributions, and quantifying the tumor response to radiotherapy based on imaging. Mechanistic patient-specific computer simulations ('digital twins') could also be used to guide adaptive radiotherapy. Overall, we are entering an era where improved modeling methods will allow the use of newly available data sources to better guide radiotherapy treatments.Copyright © 2024. Published by Elsevier Inc.