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通过使用基于深度学习的分级算法,预测前列腺癌在主动监测中的分级重新分类.

Chien-Kuang C Ding1,2, Zhuo Tony Su1, Erik Erak1

  • 1Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

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概括

一个深度学习算法,AIRAProstate,在积极监测队伍中准确识别了更高风险的前列腺癌 (PCa). 这种工具有助于更好地分层管理PCa的风险.

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科学领域:

  • 在瘤学瘤学.
  • 人工智能的人工智能
  • 病理学 病理学 病理学

背景情况:

  • 前列腺癌的深度学习 (DL) 算法 (PCa) 活检幻灯片上的等级组 (GG) 确定缺乏临床结果验证.
  • 精确的分级对于管理PCa至关重要,特别是在主动监控 (AS) 协议中.

研究的目的:

  • 验证基于DL的算法 (AIRAProstate) 在独立PCa主动监测队伍中对前列腺活检进行重新分级的实用性.
  • 评估基于DL的重新分类与AS期间等级重新分类等临床结果的关联.

主要方法:

  • 利用AIRAProstate DL算法从两个独立的PCa AS队列重新评估初始前列腺活检.
  • 分析了基于AIRA的前列腺升级 (至GG≥2) 和AS随后的等级重新分类之间的关联.
  • 在一个队列中,以DL为基础的升级与当代泌尿病理学家的评论进行了比较.

主要成果:

  • 在第一个队列 (n=138,初始GG1) 中,AIRA前列腺升级与AS (OR=3.3,P=0.04) 的等级重新分类有显著的关联,与泌尿病学家的评论不同.
  • 在验证队列 (n=169,所有初始GG1) 中,AIRA前列腺升级也预测了AS的等级重新分类 (HR=1.7,P=0.03).

结论:

  • 基于DL的AIRAProstate算法在预测活跃监测前列腺癌患者的等级重新分类方面具有显著的实用性.
  • 这种人工智能工具有望改善PCa管理中的风险分层和临床决策.