基于淋巴细胞细分,分类和病态特征的临床结果预测在最小变化疾病和焦点细分淋巴细胞硬化症的临床结果
Akhil Ambekar1,2, Maryam Roohian1, Qian Liu3
1Division of AI and Computational Pathology, Department of Pathology, Duke University, Durham, North Carolina, USA.
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
计算病理学准确地量化了质硬化症 (GS) 和细分硬化症 (SS) 在脏疾病中. 这种方法还揭示了非GS/SS球体的新视下特征,可以更好地预测疾病的进展和缓解.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 计算病理学计算病理学
- 数字健康数字健康
背景情况:
- 焦点细分样硬化症 (FSGS) 和最小变化疾病 (MCD) 的评估传统上依赖于量化全球 (GS) 和细分硬化症 (SS).
- 非GS/SS质细胞含有通过标准组织学检查看不到的预后信息.
- 这项研究探讨了计算图像分析,从脏活检中提取新的预后标记.
研究的目的:
- 通过深度学习自动化淋巴细胞细分和分类成GS,SS和非GS/SS类别.
- 从非GS/SS质体中提取和评估视下病态特征的临床相关性.
- 为了比较计算病理的预后性能与传统的手动评分,用于病进展和蛋白尿症缓解.
主要方法:
- 开发了深度学习模型,用于定期酸 Schiff 染色全片图像上的质细胞分类.
- 计算机辅助的%GS和%SS得分与人类得分进行了比较,以确定与临床结果的关联.
- 从非GS/SS质细胞中提取并分析了病态特征,使用受罚的Cox回归和最大相关性最小冗余.
- 评估了针对疾病进展和蛋白尿症缓解的选择性病态特征的预后价值.
主要成果:
- 计算机辅助的质细胞评分显示,%GS (ICC=0.889) 的良好一致,%SS (ICC=0.592) 的中度一致.
- 使用计算病理学的预后模型显示,疾病进展和蛋白尿症缓解的手动评分的性能与手动评分相当.
- 从非GS/SS质细胞中提取的病态特征显示出适度的预后性能,两个特征在调整后与蛋白尿症缓解显著相关.
结论:
- 计算病理学为预测脏疾病结果提供了SS/GS质细胞的自动定量,与手动评估可比.
- 这种方法从以前未被识别的非GS/SS质体中发现了临床上有用的信息.
- 数字病理学通过整合视下特征来提高FSGS和MCD的预后准确性.
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