可解释机器学习用于尿路细胞分类和尿细胞学中的风险评分
Lei Xiong1, Xinyi Cao2, Yu Shang3
1Department of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
iScience
|January 16, 2026
概括
一个新的机器学习工具准确地分类尿细胞,并预测高度尿癌 (UC) 风险. 这种可解释的框架提高了数字尿细胞学中的一致性和效率.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 计算病理学计算病理学
- 人工智能的人工智能
背景情况:
- 尿细胞学是尿癌 (UC) 的关键诊断工具.
- 目前的方法面临着灵敏度和观察者间变异性的局限性.
- 需要更客观和可重复的诊断方法.
研究的目的:
- 开发一种可解释的机器学习 (ML) 框架,用于尿细胞分类.
- 估计高度UC的滑动水平风险.
- 提高数字尿细胞学的一致性,效率和可解释性.
主要方法:
- 根据巴黎体系的标准,从10230个专家注释的尿细胞中提取了20个定量特征.
- 经过训练和验证的顺序逻辑回归和随机森林模型用于细胞分类.
- 在247个案例的验证集中,从聚合的单元概率中推导出滑动级风险得分.
主要成果:
- ML模型在将细胞分类为正常,非典型或可疑的准确度超过90%.
- 可以解释的形态特征被确定为关键预测因素.
- 衍生风险得分有效地将病例分为负面,非典型,低级和高级UC类别 (p < 0.0001).
结论:
- 开发的基于特征的ML框架为尿细胞学提供了一种透明和定量方法.
- 这种方法有可能提高诊断的一致性和效率.
- 该框架与已建立的细胞学标准保持一致,有助于解释数字尿细胞学发现.
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