数据科学改善放疗规划和临床决策的机遇

IF 2.6 3区 医学 Q3 ONCOLOGY
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引用次数: 0

摘要

放疗的目的是在尽量减少对正常组织损伤的同时,实现较高的肿瘤控制概率。因此,针对个体患者的个性化放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。可以利用各种丰富的数据源(包括肿瘤和正常组织基因组学、放射组学和剂量组学)改进预测模型。深度学习将推动改进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根据成像量化肿瘤对放疗的反应。针对特定患者的机理计算机模拟("数字双胞胎")也可用于指导自适应放疗。总之,我们正在进入这样一个时代:通过改进建模方法,可以利用新获得的数据源更好地指导放疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Science Opportunities To Improve Radiotherapy Planning and Clinical Decision Making

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.

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来源期刊
CiteScore
5.80
自引率
0.00%
发文量
48
审稿时长
>12 weeks
期刊介绍: Each issue of Seminars in Radiation Oncology is compiled by a guest editor to address a specific topic in the specialty, presenting definitive information on areas of rapid change and development. A significant number of articles report new scientific information. Topics covered include tumor biology, diagnosis, medical and surgical management of the patient, and new technologies.
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