开发和验证可解释的机器学习模型,以区分良性和恶性乳腺癌
Zhichun Wang1, Weixiang Liu2, Lin Hua3
1Department of Breast Surgery, Jiujiang City Key Laboratory of Cell Therapy/Human Genetic Resources Innovation Center, The First People's Hospital of Jiujiang City, Jiujiang, China.
International journal of medical informatics
|January 27, 2026
概括
使用常规临床和实验室数据的新物流模型有效地区分恶性和良性乳腺病变. 这种工具有助于早期风险分层,可能减少用于乳腺癌检测的不必要的活检.
科学领域:
- 在瘤学瘤学.
- 医学诊断 医学诊断 医学诊断
- 生物统计学 生物统计学
背景情况:
- 早期发现乳腺癌对于患者的预后至关重要.
- 区分恶性和良性乳腺病变往往需要像活检这样的侵入性手术.
- 需要使用非侵入性方法来支持临床决策.
研究的目的:
- 开发和外部验证乳腺病变恶性瘤的预测模型.
- 为了利用常规的临床和实验室变量进行风险分层.
- 为了减少不必要的乳腺活检率.
主要方法:
- 一个回顾式的两中心研究与发展 (N=745) 和外部 (N=221) 队列.
- 在固定测试和外部集上进行交叉验证和评估的后勤回归模型开发.
- 性能指标包括AUC,灵敏度,特异性,F1得分,布里尔得分,校准曲线,DCA和SHAP分析.
主要成果:
- 物流回归模型选择了年龄,TT,APTT,CEA和Ca作为关键预测因素.
- 实现了高性能,交叉验证AUC为0.910和外部验证AUC为0.861.1.
- 决策曲线分析表明显著的临床净益,SHAP确定了关键的贡献因素.
结论:
- 使用常规实验室变量的后勤模型有效地区分恶性与良性乳腺病变.
- 该模型显示了强大的外部性能和早期风险分层的明显临床实用性.
- 这种工具可以帮助尽量减少不必要的活检,并改善乳腺癌管理.
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