数字衍生的Ki-67扩散指数用于胃肠胰腺神经内分泌瘤
Tamás Micsik1,2, Lilla Csellár1,2, Árpád V Patai2,3
1Department of Pathology and Experimental Cancer Research, Semmelweis University, Budapest, Hungary.
Pathology oncology research : POR
|January 23, 2026
概括
自动化Ki-67增殖指数评估准确地对胃肠瘤神经内分泌瘤 (GEPNENs) 进行评分,改进了手动方法. 这种数字病理学方法提高了精度,并减少了GEPNEN分级的变化,以获得更好的患者结果.
科学领域:
- 数字病理学数字病理学
- 在瘤学瘤学.
- 病理学 病理学 病理学
背景情况:
- 基-67增殖指数 (PI) 对于分类胃肠神经内分泌瘤 (GEPNEN) 和指导治疗决策至关重要.
- 手动Ki-67计数是耗时的,容易引起观察者间的变化,可能导致错误评分和不良患者结果.
- 数字病理学提供了先进的工具来克服手动评估的局限性.
研究的目的:
- 评估传统的临床分级,使用MarkerCounter (MC) 的手动计数,以及使用PatternQuant和NuclearQuant (NQ) 的自动分级对GEPNEN中Ki-67 PIs的等价性.
- 评估基于机器学习的数字病理学方法的准确性和可靠性,用于GEPNEN等级.
主要方法:
- 对60例手术切除的GEPNEN病例进行了回顾性分析.
- 传统的临床分级 (C) 与MarkerCounter (MC) 和NuclearQuant (NQ) 自动评估的比较.
- 利用3DHistech的数字病理学平台进行分析.
主要成果:
- 在分级方法之间观察到的高度一致性:斯皮尔曼相关性 (C与MC: ρ = 0.912,C与NQ: ρ = 0.883,MC与NQ: ρ = 0.953).
- 数字PI值的密切相关性 (线性回归:C与MC:r = 0.952,C与NQ:r = 0.925,MC与NQ:r = 0.978). 线性回归:C与MC:r = 0.952,C与NQ:r = 0.925,MC与NQ:r = 0.978).
- 自动评估分析了更多的瘤细胞,提供了更强大的全球PI,并确定了高度瘤中的细胞形态差异.
结论:
- 使用数字病理学平台进行的自动化Ki-67 PI评估证明了与GEPNEN分级手动方法的高度等价性.
- 这种基于机器学习的方法为人工计数提供了一个精确,高效和可重复的替代方案,有可能改善GEPNEN的管理.
- 瘤细胞核的细胞形态参数也可以作为GEPNEN分级和预后的宝贵工具.
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