探索非特异性症状患者癌症风险的血清生物标志物小组:特征选择方法的比较分析
Maria Jose Monroy-Iglesias1, Aida Santaolalla2, Sabine Martin3
1Transforming Cancer Research Outcomes Through Research (TOUR), King's College London, London, UK maria.j.monroy_iglesias@kcl.ac.uk.
BMJ open
|December 10, 2025
概括
生物标志物得分有效识别患有非特异性症状 (NSS) 的患者,患有更高癌症风险. 这些分数有助于有针对性的诊断,并可能改善快速诊断诊所 (RDC) 的分拣.
科学领域:
- 在瘤学瘤学.
- 生物标志物发现发现
- 诊断的准确性 诊断的准确性
背景情况:
- 在患有非特异性症状 (NSSs) 的患者中诊断癌症的延迟会导致不良结果.
- 快速诊断诊所 (RDC) 旨在加快护理,但大多数NSS患者没有癌症,需要更好的风险分层.
- 目前NSS患者的诊断途径需要优化,以有效检测癌症.
研究的目的:
- 开发和验证基于生物标志物的临床预测得分,用于NSS患者的风险分层.
- 为了更有针对性的诊断方法,区分高风险和低风险NSS患者.
- 提高快速诊断诊所 (RDC) 的诊断产量和效率.
主要方法:
- 在伦敦的一家中等护理RDC中进行了回顾性和前性队列研究.
- 基于生物标志物的风险评分是使用隐性类分析 (LCA) 和最小绝对收缩和选择操作员 (LASSO) 方法开发的.
- 模型性能使用逻辑回归,接收器操作特征曲线 (AUROC) 和决策曲线分析进行了评估,并进行了外部验证.
主要成果:
- LCA确定了高白细胞计数,低血红蛋白,低白蛋白,高血清兰巴细胞轻链,高中性粒细胞与淋巴细胞的比率,高血清卡帕细胞轻链 (SKLC),高红细胞沉积率 (ESR),高C反应蛋白 (CRP) 和高中性粒细胞作为癌症风险标志物.
- 拉索选择了高血小板,ESR,CRP,SKLC,性酸酶和乳酸脱酶.
- 无论是LCA和LASSO得分都显著预测了更高的癌症几率,具有良好的区分能力 (AUROC:LCA 0.74;LASSO 0.73). 外部验证证实了LASSO得分的预测价值.
结论:
- 从LCA和LASSO获得的基于生物标志物的得分有效地确定了NSS患者的癌症风险较高.
- LCA提供了更广泛的生物标志物含量和更高的灵敏度,而LASSO使用了更少的标志物,具有更高的特异性.
- 这些得分显示了改善RDC分类和检测严重良性疾病的潜力,为临床实施提供了进一步的验证.
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