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使用深度学习生存模型从突变基因确定肺癌的预后:一项大型多中心研究
Jie Peng1, Lushan Xiao2, Hongbo Zhu3
1Department of Medical Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China. sank44@sina.com.
Cancer cell international
|November 5, 2023
概括
一种新的深度学习生存 (DLS) 模型使用突变基因数据准确预测肺癌患者的预后. 这种DLS模型在免疫疗法和非免疫疗法治疗的患者中,与现有方法相比,显示出更高的性能.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 人工智能在医学中的应用
背景情况:
- 基因状态对于预测癌症患者预后至关重要.
- 深度学习生存 (DLS) 模型是瘤学医学成像分析的新兴工具.
- 关于DLS模型的研究有限,这些模型利用突变基因来预测整体存活率 (OS) 或无进展存活率 (PFS),特别是在免疫治疗 (IO) 的背景下.
研究的目的:
- 开发和验证一种深度学习生存 (DLS) 模型,用于预测肺癌患者的预后.
- 评估DLS模型在接受或不接受免疫疗法 (IO) 的患者中的疗效.
- 评估DLS模型的预测性能与已建立的预后标志物相比.
主要方法:
- 来自6542名肺癌患者的样本的基因组测序.
- 基于多面板体质突变的DLS模型的培训和验证.
- 该模型经过训练,可以在多个队列中预测非IO患者的OS和IO患者的PFS.
主要成果:
- 在多个验证队列中,DLS模型证明了在没有IO的患者中对OS的可靠预测.
- 在用IO治疗的患者中,DLS模型准确预测了PFS和OS.
- DLS模型实现了最高的C指数,超过了瘤结节转移阶段测定,COX模型,瘤突变负担和PD-L1表达.
结论:
- 一个利用突变基因数据的DLS模型为预测肺癌预后提供了一个强大的方法.
- 开发的DLS模型对免疫治疗和非免疫治疗的肺癌患者都有效.
- 这种DLS方法在个性化癌症预后方面取得了重大进展.
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