在白血病骨髓环境中,正常的造血干细胞经历了可以通过人工智能识别的形态变化
Dongguang Li1, Athena Li2, Ngoc DeSouza1
1Division of Hematology/Oncology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.
International journal of molecular sciences
|November 13, 2025
概括
人工智能现在可以在白血病骨髓中的非白血病造血干细胞 (HSC) 中识别明显的形态变化. 这种人工智能驱动的方法为监测疾病严重程度和预测患者治疗反应提供了一种新方法.
科学领域:
- 血液学 血液学 血液学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 白血病干细胞 (LSCs) 驱动血液恶性瘤,影响骨髓中的非白血病造血干细胞 (HSCs).
- 假设非白血病HSC的形态变化与疾病严重程度和治疗结果相关.
- 目前的方法无法检测到这些微妙的形态干细胞变化.
研究的目的:
- 确定人工智能 (AI) 深度学习是否可以在白血病环境中识别和量化非白血病HSC.
- 调查非白血病HSC与正常HSC相比是否表现出AI可识别的形态差异.
- 建立一个概念验证,用于使用人工智能来评估疾病预后和治疗反应.
主要方法:
- 使用一种具有JAK2V617F瘤基因的多细胞血症真菌 (PV) 鼠标模型.
- 应用AI深度学习来分析骨髓细胞的单细胞图像.
- 量化并区分非JAK2V617F HSC,LSC和正常的HSC.
主要成果:
- 人工智能精确地区分了非JAK2V617F HSC 与LSC 的区别 (>96%的准确率).
- 人工智能发现了来自光伏小鼠的非JAK2V617F HSC 和正常的HSC (>98%准确率) 之间的显著形态差异.
- 证明非白血病HSC在白血病微环境中经历了AI可检测的形态变化.
结论:
- 非白血病HSC在白血病骨髓中显示独特的,AI可识别的形态特征.
- 人工智能对HSC形态的分析具有评估血液瘤治疗反应和预后的潜力.
- 这种方法可能会导致改善PV等疾病的患者管理策略.
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