基于人工智能的数字图像分析,用于评估质母细胞瘤多形体中的Ki67,P53和PHH3表达
Tuba Devrim1, Gamze Erkilinc1, Saniye Sevim Tuncer1
1Department of Medical Pathology, Cigli Training and Research Hospital, Izmir Bakircay University, Izmir, Turkiye.
Journal of the College of Physicians and Surgeons--Pakistan : JCPSP
|February 14, 2026
概括
人工智能 (AI) 对质母细胞瘤多种类型 (GBM) 中的Ki67,PHH3和p53生物标志物的分析显示出强烈的一致性和显著的相关性,优于传统病理学家的评估. 这突出了人工智能.
科学领域:
- 在瘤学瘤学.
- 病理学 病理学 病理学
- 生物医学工程 生物医学工程
背景情况:
- 多形质母细胞瘤 (GBM) 是一种具有侵略性的脑瘤,需要精确的生物标志物评估.
- 像Ki67,PHH3和p53这样的免疫组织化学 (IHC) 生物标志物对于GBM的诊断和预后至关重要.
- 对IHC生物标志物的传统病理评估可能是主观的,并且在观察者之间有所不同.
研究的目的:
- 将基于人工智能 (AI) 的基67,PHH3和GBM中的p53的IHC分析与传统病理学家评估进行比较.
- 评估AI和病理学家评估在不同组织采样领域的一致性和统计意义.
- 评估AI在改善GBM生物标志物评估的客观性方面的潜力.
主要方法:
- 一个涉及20个GBM病例的横截面分析研究.
- 在甲固定,嵌的组织截面上对Ki67,PHH3和p53进行IHC染色.
- 数字化染色幻灯片,由两名专家病理学家和基于人工智能的图像分析软件对1毫米2和7毫米2面积进行评估.
- 统计分析使用科恩的卡帕协议和斯皮尔曼的相关性协会.
主要成果:
- 人工智能和病理学家的评估都表明,在不同领域的大小中,方法内部的一致性很强.
- 人工智能分析显示,Ki67和PHH3之间以及PHH3和p53之间存在显著的相关性 (p <0.05).
- 在病理学家评估中,没有发现这些标志物之间的显著相关性 (p>0.05).
结论:
- 人工智能支持的数字病理学为像GBM这样的侵略性瘤的生物标志物评估的客观性和个性化提供了潜在的改善.
- 与传统方法相比,人工智能分析表明,与传统方法相比,人工智能更有能力识别关键生物标志物之间的相关性.
- 这些发现表明,人工智能可以提高GBM生物标志物评估的诊断准确性和一致性.
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