身体组成作为评估未来肺癌风险的生物标志物
medRxiv : the preprint server for health sciences
|November 1, 2024
概括
使用低剂量计算机断层扫描 (LDCT) 测量身体成分可以预测肺癌风险. 这一发现可以通过优化患者的资格和查频率来提高肺癌查,以便早期检测.
科学领域:
- 在瘤学瘤学.
- 放射学 放射学是一门学科.
- 生物标志物 生物标志物
背景情况:
- 低剂量计算机断层扫描 (LDCT) 的肺癌查对于早期检测至关重要.
- 识别患肺癌高风险的个体对于有效的查策略至关重要.
- 身体成分作为肺癌风险的潜在生物标志物的作用需要进一步研究.
研究的目的:
- 为了确定通过LDCT评估的身体成分是否可以作为预测肺癌风险的生物标志物.
- 探索身体组成指标在提高肺癌查计划的有效性方面的实用性.
主要方法:
- 使用了匹兹堡肺部查研究 (PLuSS) 和NLST-ACRIN队列的LDCT扫描.
- 开发了人工智能 (AI) 算法,用于自动细分和量化身体关键组织:皮下脂肪组织 (SAT),内脏脂肪组织 (VAT),肌内脂肪组织 (IMAT),骨肌肉 (SM) 和骨.
- 采用特定原因的考克斯比例危险模型和时间依赖的接收器运行特征 (ROC) 分析来评估风险和预测能力.
主要成果:
- 结合年龄,吸烟状况,骨体积,SM密度,IMAT比率,IMAT密度和SAT体积的复合模型显示出预测能力.
- 在PLuSS队列中训练的模型在21年内实现了0.76的曲线下面积 (AUC),并在7年内实现了0.70的肺癌预测.
- 在NLST队列上验证的模型显示AUC在7年的随访期间从0.61到0.68不等.
结论:
- 来自LDCT的身体组成指标是肺癌风险的重要预测指标.
- 将身体成分分析纳入LDCT查可以优化资格标准和查频率,从而提高查的有效性.
- 结合人口因素和身体组成特征的预测模型可以有效地识别患肺癌风险较高的个体.
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