Department of Medical Physics, Attending Physicist, Chief, Service for Predictive Informatics, Chair, Memorial Sloan Kettering Cancer Center, New York, NY..
Semin Radiat Oncol. 2024 Oct;34(4):379-394. doi: 10.1016/j.semradonc.2024.07.012.
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.
放射治疗旨在实现高肿瘤控制概率，同时将对正常组织的损伤降至最低。因此，为个体患者实现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。可以使用广泛的丰富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。深度学习将推动改善正常组织耐受分类、预测治疗期间肿瘤变化、跟踪累积剂量分布以及基于成像量化肿瘤对放射治疗的反应。也可以使用针对患者个体的机制计算机模拟（“数字双胞胎”）来指导自适应放射治疗。总的来说，我们正进入一个新时代，在这个时代，改进的建模方法将允许使用新出现的数据来源来更好地指导放射治疗。
