癌细胞的形态多样性预测了整个瘤类型的预后
Rasoul Sali1, Yuming Jiang1,2, Armin Attaranzadeh1
1Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
Journal of the National Cancer Institute
|November 20, 2023
概括
在例行病理学图像中量化癌细胞多样性提供了一种预测患者结果的新方法. 这种计算方法揭示了瘤异质性,改善了个性化癌症治疗策略的风险分层.
科学领域:
- 计算病理学计算病理学
- 癌症基因组学 癌症基因组学
- 数字组织病理学 数字组织病理学
背景情况:
- 瘤内部异质性是癌症进展和治疗耐药性的关键驱动因素.
- 在组织病理学图像中量化癌细胞多样性对于了解患者的结果至关重要.
研究的目的:
- 开发和验证一种计算方法来量化癌细胞多样性,使用常规的血素-素染色基因病理学图像.
- 评估这种多样性得分在预测多种癌症类型患者结果中的预后价值.
主要方法:
- 分析了2000张肺癌,头癌,结肠癌和直肠癌的数字化整片图像.
- 深度卷积自编码器训练用于单细胞核特征提取.
- 计算内核变异性和内核多样性特征以量化瘤异质性.
- 开发一种机器学习模型,根据形态多样性预测患者的预后.
主要成果:
- 对6800万个癌细胞的细分和分析揭示了15-20个并存的形态亚型.
- 较高的形态多样性与染色体不稳定性和基因组异位积分相关.
- 基于形态多样性的机器学习模型显示了独立的预后值 (HR 1.62-3.23,P < .035) 和结合临床因素时改善的预后.
结论:
- 已经建立了一种实用的计算方法,用于从常规组织病理学图像中量化瘤内部异质性.
- 开发的癌细胞多样性得分可以提高风险分层,并指导个性化治疗策略.
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