正常组织毒性预测:临床翻译在地平线上
Sarah L Kerns1, William A Hall1, Brian Marples2
1Department of Radiation Oncology, the Medical College of Wisconsin, Milwaukee, WI.
Seminars in radiation oncology
|June 18, 2023
概括
预测正常组织的放射敏感性可以个性化放射治疗,减少癌症幸存者的长期副作用. 结合患者数据和人工智能的先进模型显示出临床使用的前景.
科学领域:
- 在瘤学瘤学.
- 辐射瘤学 辐射瘤学
- 医学物理 医学物理
背景情况:
- 放射治疗的进步增加了癌症幸存率,但带来了晚期毒性的风险.
- 目前无法预测个体的放射性敏感性会影响生活质量,并限制剂量升级.
研究的目的:
- 审查用于正常组织放射性毒性的预测性测试.
- 突出在开发预测模型临床实用性的证据方面取得的进展.
主要方法:
- 晚期放射性毒性的多因素病因的审查.
- 开发预测模型,整合治疗,人口统计,并发病和生物数据.
- 人工智能 (AI) 的应用用于数据分析和模型开发.
主要成果:
- 结合多种数据类型的多变量模型正在发展.
- 人工智能有助于从大型数据集中提取信号,用于预测建模.
- 模型正在进行临床试验以进行评估.
结论:
- 预测正常组织辐射敏感性的测试对于个性化放射治疗至关重要.
- 人工智能驱动的预测模型有望减少晚期毒性并改善治疗指数.
- 预计这些模型将在临床上被采用,以指导治疗决策和随访.
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