数据科学的机会改善放射治疗计划和临床决策
Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making
影响因子:3.20000
分区:医学3区 / 肿瘤学3区 核医学3区
发表日期:2024 Oct
作者:
Joseph O Deasy
摘要
放射治疗旨在达到高肿瘤控制概率，同时最大程度地减少对正常组织的损害。因此，针对个别患者的个性化放疗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来源（包括肿瘤和正常组织基因组学，放射组学和不同剂量组学）改善预测模型。深度学习将推动对正常组织耐受性分类，预测治疗内肿瘤的变化，跟踪累积剂量分布以及基于成像对放射疗法的肿瘤反应进行量化。机械性患者特定的计算机模拟（“数字双胞胎”）也可以用于指导适应性放射疗法。总体而言，我们进入一个时代，改进的建模方法将允许使用新可用的数据源来更好地指导放射治疗。
Abstract
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.