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多模式整合纵向非侵入性诊断以预测免疫治疗中的存活率,使用深度学习
Melda Yeghaian1,2,3, Zuhir Bodalal2,3, Daan van den Broek4
1Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen 6525 GD, The Netherlands.
概括
人工智能通过使用非侵入性数据预测患者的生存率来增强癌症免疫疗法. 一个新的深度学习模型,MMTSimTA,整合了血液测试,药物和CT扫描,以改善个性化的预后.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 免疫疗法已经改变了癌症治疗,但预测患者的反应仍然具有挑战性.
- 了解晚期癌症的反应模式对于个性化医学至关重要.
- 借助人工智能,利用常规收集的非侵入性数据,提供了一条改善免疫治疗结果的途径.
研究的目的:
- 开发和评估一种新型的人工神经网络,用于预测免疫治疗癌症患者的死亡率.
- 评估整合多式联络,纵向,非侵入性数据用于癌症预后的有效性.
- 探索AI在个性化癌症治疗策略方面的潜力.
主要方法:
- 开发了一个基于多式变压器的简单时间注意力 (MMTSimTA) 网络.
- 治疗前和治疗期间的综合血液测量,药物和基于CT的器官体积.
- 在694名接受免疫治疗的癌症患者的泛癌队列上验证了该模型.
主要成果:
- MMTSimTA模型在3,6,9和12个月的生存预测中取得了强大的预后性能.
- 曲线下的面积 (AUC) 值在0.81至0.84之间,表明预测准确度高.
- 与基线方法相比,该模型显示出更高的性能.
结论:
- 将非侵入性纵向数据与新型MMTSimTA架构集成,可以提高多式联络预后性能.
- 基于深度学习的多模式纵向数据集成显示了个性化癌症预后的前景.
- 这种方法可以帮助为个别患者量身定制免疫治疗策略.
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