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Brain Abscess l: Introduction01:26

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A brain abscess is a focal, intracerebral infection characterized by a localized collection of pus within the brain parenchyma, resulting from microbial invasion and the body’s inflammatory response. It progresses through stages: early and late cerebritis, followed by early and late capsule formation, reflecting tissue destruction, immune response, and eventual encapsulation.Etiology and PathogenesisCausative organisms vary with source and host factors, often involving polymicrobial infections,...

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基于双能量CT衍生图的深度学习分析,用于预测胃癌中PD-L1表达:一项多中心研究

Lihong Chen1, Yuncong Zhao2, Xiaomin Tian3

  • 1Department of Radiology, Fujian Medical University Union Hospital, Fuzhou 350001, China (L.C., Y.C., Y.X.); The School of Medical Imaging, Fujian Medical University, Fuzhou 350100, China (L.C., Y.Z., S.L., K.C., Y.X.); Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors (Fujian Medical University), Fuzhou 350001, China (L.C., Y.X.).

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概括

这项研究表明,使用双能量CT图的深度学习模型可以准确地预测PD-L1表达在胃癌 (GC) 中的非侵入性. 该工具有助于指导GC患者的免疫治疗决策.

关键词:
深度学习 (Deep Learning) 是一种深度学习.双能量CT是双能量CT.胃癌 胃癌 胃癌 胃癌地图 地图在PD-L1中.

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科学领域:

  • 在瘤学瘤学.
  • 放射学 放射学是一门学科.
  • 人工智能的人工智能

背景情况:

  • 编程死亡连接体1 (PD-L1) 表达是预测胃癌 (GC) 免疫治疗反应的关键生物标志物.
  • 准确预测PD-L1表达对于优化GC治疗策略至关重要.
  • 目前评估PD-L1表达的方法可能是侵入性的或耗时的.

研究的目的:

  • 评估深度学习 (DL) 模型的有效性,该模型使用双能CT (DECT) 衍生的图来非侵入性地预测GC中的PD-L1表达水平.
  • 将DL模型的性能与传统的临床模型和联合DL-临床模型进行比较.

主要方法:

  • 一项多中心前性研究招募了267名接受DECT和胃切除术的GC患者.
  • 一个50层的残余网络被用来从图中提取DL特征.
  • 一个DL特征签名模型 (DFSigM) 已被开发和内部和外部验证,以及临床和融合模型.

主要成果:

  • DFSigM表现出强大的预测性能,AUC为0.854 (训练),0.836 (内部验证) 和0.818 (外部验证).
  • DFSigM超越了临床模型,并显示了与DL临床融合模型相似的结果.
  • 使用SHAP和Grad-CAM实现了模型解释性,可视化了决策过程.

结论:

  • 对DECT衍生图的深度学习分析为GC中非侵入性PD-L1表达预测提供了有价值,可靠和可解释的方法.
  • 这种方法有可能改善患者选择免疫疗法,并指导GC管理中的临床决策.