利用数据科学改善放射治疗计划和临床决策的机遇
Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making
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影响因子:3.2
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
DOI:
10.1016/j.semradonc.2024.07.012
摘要
放射治疗旨在实现高肿瘤控制概率，同时最小化对正常组织的损伤。因此，为个体患者制定个性化放射治疗方案，依赖于将物理治疗计划与肿瘤控制及正常组织并发症的预测模型相结合。预测模型可以利用包括肿瘤和正常组织基因组学、放射组学（radiomics）和剂量组学（dosiomics）在内的丰富数据源进行优化。深度学习将推动正常组织耐受性分类、治疗中的肿瘤变化预测、累积剂量分布追踪以及基于影像的肿瘤反应量化。机制性个体化计算机模拟（“数字孪生”）也可用于指导适应性放疗。总体而言，随着新型数据源的不断出现，改进的建模方法将使我们更好地指导放疗治疗。
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.