血清前列腺特异性抗原和循环炎症标记物的诊断实用性,用于区分前列腺癌与良性前列腺增生
1Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University Xi'an 710061, Shaanxi, China.
American journal of cancer research
|December 15, 2025
概括
一个新的机器学习模型使用诸如中性粒细胞-淋巴细胞比率 (NLR) 和中性粒细胞-脂蛋白A1比率 (NAR) 等炎症标志物,显示前列腺癌 (PCa) 诊断比PSA和PHI更好. 这种方法可以减少不必要的活检.
科学领域:
- 泌尿器科 泌尿器科 泌尿器科 泌尿器科
- 在瘤学瘤学.
- 生物标志物 生物标志物
背景情况:
- 血清前列腺特异性抗原 (PSA) 和前列腺健康指数 (PHI) 用于前列腺癌 (PCa) 检测.
- 周围血液的炎症标志物有可能提高诊断准确度.
研究的目的:
- 评估PSA,PHI和炎症标志物 (NLR,LMR,NAR,ApoA1) 在区分PCa与良性前列腺增生 (BPH) 的诊断性能.
- 为PCa诊断开发一个优化的机器学习 (ML) 模型.
主要方法:
- 701名接受前列腺活检的患者的回顾性分析.
- 开发和验证五个ML模型 (逻辑回归,决策树,随机森林,SVM,XGBoost) 使用LASSO回归进行特征选择.
- 使用ROC曲线,校准图,障碍得分和决策曲线分析 (DCA) 的性能评估.
主要成果:
- 与BPH患者相比,PCa患者的PSA,NLR,NAR和PHI水平较高,ApoA1和LMR水平较低 (P<0.05).
- XGBoost模型实现了最高的诊断准确性 (测试AUC:0.979),显著超过PSA和PHI.
- 在XGBoost模型中,NLR和NAR是关键预测因素,通过DCA证明了优异的校准和临床实用性.
结论:
- 集成NLR,LMR和NAR的XGBoost模型显示PCa的诊断性能优于传统标记物.
- 这种基于ML的方法有潜力增强剖析前风险分层,减少侵入性手术.
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