生物学引导的深度学习预测了预后和癌症免疫治疗反应
Yuming Jiang1,2, Zhicheng Zhang2,3, Wei Wang4
1Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Nature communications
|August 23, 2023
概括
这项研究引入了一种新的生物学引导的深度学习模型,用于预测癌症治疗反应和医学图像的结果. 该方法通过解释瘤微环境和指导治疗选择来增强个性化医疗.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 深度学习在癌症检测方面表现有前途,但在预测治疗反应方面存在困难.
- 当前模型中缺乏可解释性阻碍了临床翻译,因为它与病理生物学脱节.
- 个性化治疗策略需要准确预测患者的结果.
研究的目的:
- 开发一种以生物学为导向的深度学习方法,用于预测瘤微环境状态和治疗结果.
- 能够同时预测预后和治疗,从医学图像中获益.
- 提高深度学习在瘤学的临床实用性.
主要方法:
- 开发了一个新的生物学引导的深度学习模型.
- 该模型同时预测瘤免疫和树皮微环境状态.
- 模型验证是使用多中心国际数据进行的,用于胃癌预后和化疗效益.
主要成果:
- 该模型准确地预测了胃癌的预后和辅助化疗效益.
- 它预测了对免疫检查点抑制剂的反应,补充了现有的生物标志物.
- 该模型识别了不响应免疫治疗的不匹配修复缺陷瘤.
结论:
- 生物学引导的深度学习提供了对癌症治疗结果的可解释和准确的预测.
- 这种方法可以指导个性化治疗策略,并改善患者选择治疗方法.
- 该模型有可能为不响应瘤的组合治疗提供信息.
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