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基于成人炎症和营养指标预测非小细胞肺癌的机器学习:一项跨部门研究

Qiaoli Wang¹, Tao Liang², Yuexi Li¹

¹Department of Health Screening Center, Deyang Peoples' Hospital, Deyang, Sichuan, 618000, People's Republic of China.

Cancer management and research

|June 4, 2024

概括

血液炎症标志物可以帮助诊断非小细胞肺癌 (NSCLC). 使用血小板分布宽度的机器学习模型在预测成人NSCLC方面取得了很高的准确性.

科学领域:

在瘤学瘤学.
生物标志物生物标志物
机器学习在医学中的应用

背景情况:

非小细胞肺癌 (NSCLC) 的诊断可能具有挑战性,特别是在无症状个体中.
血液中的炎症和营养标志物可以提供有关癌症发展的见解.
机器学习为分析复杂的生物标记数据提供了一种新的方法.

研究的目的:

评估血液炎症标志物对非小细胞肺癌 (NSCLC) 的诊断价值.
开发和验证用于预测无症状成年人NSCLC的机器学习模型.
为了确定关键的炎症和营养标记,相关的NSCLC检测.

主要方法:

一项涉及139名NSCLC患者和198名健康对照者的横截面研究.
分析炎症/营养标志物 (例如单细胞,中性粒细胞,LMR,NLR,PLR,PHR) 和NSCLC之间的相关性.
使用Python库进行功能选择,并通过五种机器学习算法进行分析.
模型性能评估使用精度,准确性,回忆,F1得分和AUC.

主要成果:

最重要的14个预测因素包括血小板分布宽度 (PDW),年龄和各种血细胞计数/比率.
纯粹的贝叶斯 (NB) 算法在测试模型中表现最好.
该NB模型的准确度为0.87,宏观F1得分为0.85,加权F1得分为0.87,AUC为0.84.

结论:

血小板分布宽度 (PDW) 成为NSCLC预测中最重要的特征.
纯粹的贝叶斯机器学习算法在预测成年NSCLC方面表现出卓越的性能.
基于血液的炎症标志物有潜力通过机器学习进行早期NSCLC诊断.

关键词:

诊断诊断诊断诊断诊断诊断炎症指标可以显示出炎症指标.机器学习是机器学习.非小细胞肺癌的肺癌.营养指标营养指标这是一个比率比率的比率.

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