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相关概念视频

Tumor Immunotherapy01:27

Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
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Predictive Immune Modeling of Solid Tumors
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弱监督的深度学习预测了基于PD-L1表达的固体瘤中的免疫治疗反应.

Marta Ligero1, Garazi Serna2, Omar S M El Nahhas3

  • 1Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.

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|December 21, 2023
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概括
此摘要是机器生成的。

一种新的深度学习 (DL) 方法可以从免疫组织化学 (IHC) 图像中准确预测被编程的死亡配体1 (PD-L1) 状态. 这种方法改善了癌症免疫疗法的患者分层,超出了传统的评分方法.

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科学领域:

  • 计算病理学计算病理学
  • 发现生物标志物的发现.
  • 癌症免疫疗法癌症免疫疗法

背景情况:

  • 编程死亡配体1 (PD-L1) 免疫组织化学 (IHC) 对于预测对癌症免疫疗法的反应至关重要.
  • 目前的量化方法 (手动和计算机辅助) 在可重现性和预测性方面存在局限性.
  • 准确的PD-L1评估对于有效的患者分层至关重要.

研究的目的:

  • 开发和验证深度学习 (DL) 模型,从原始IHC图像数据直接,端到端预测PD-L1状态.
  • 评估模型对免疫检查点抑制剂 (ICI) 预测反应的能力.
  • 将DL模型的性能与传统的PD-L1量化得分 (瘤比例得分[TPS]和综合阳性得分[CPS]) 进行比较.

主要方法:

  • 在PD-L1染色非小细胞肺癌 (NSCLC) 幻灯片 (MSK队列) 上训练了一种弱监督的DL模型.
  • 该模型在泛癌队列 (VHIO队列) 上得到验证.
  • 该模型预测了PD-L1表达和对ICI的反应,与TPS和CPS相比,性能较好.

主要成果:

  • DL模型在预测PD-L1表达方面表现出强的表现 (AUC在NSCLC中为0.88,在泛癌中为0.80).
  • 预测的PD-L1状态显示,与TPS (HR 1.4,P=0.082) 和CPS (HR 1.2,P=0.386) 相比,与ICI反应 (HR 1.5,P=0.049) 的相关性显著改善.
  • 可解释性分析显示,该模型整合了染色强度和形态因素.

结论:

  • 端到端弱监督的DL提供了一种强大的方法,用于从IHC图像中定量PD-L1.
  • 这种方法有可能提高癌症免疫治疗的患者分层.
  • DL模型的整体整合形态和染色强度超过了传统的评估方法.