发表机构
The University of Texas MD Anderson Cancer Center; University of California Irvine（德克萨斯大学安德森癌症中心; 加州大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有纵向生物标志物方法忽略时间间隔和固定组合的局限，提出iPEB方法，通过优化权重实现特定临床目标，在PLCO数据中显著提升肺癌风险评估灵敏度与提前时间。
AI 中文摘要
重复的血液生物标志物测量可通过捕捉单时间点分析所遗漏的纵向变化来改善癌症风险评估。参数经验贝叶斯（PEB）方法利用先前的测量值来估计个体化参考值，但现有实现未考虑测量之间的时间间隔，且依赖于具有固定组合规则的预定义标志物组合。我们开发了改进的参数经验贝叶斯（iPEB）方法，该方法考虑了系列测量之间的时间间隔，调整协变量，并进行特征选择和优化的生物标志物组合。iPEB针对特定的临床目标优化生物标志物权重，例如在预设特异性或诊断提前时间下最大化灵敏度。我们通过模拟研究和一项实际应用评估了iPEB，该应用使用了来自前列腺、肺、结直肠和卵巢（PLCO）癌症筛查试验中嵌套的病例对照研究的六种蛋白质生物标志物（pro-SFTPB、CEA、CA125、CYFRA 21-1、骨桥蛋白和HE4）。分析包括324例肺癌病例和1,674例具有至少两次系列测量的对照；使用六个中心进行模型开发，四个中心进行独立验证。在99%特异性下优化灵敏度时，iPEB在独立测试集中达到了24.2%的灵敏度，而应用于相同四标志物组合的传统PEB为18.2%。当优化提前时间时，iPEB在该严格操作点增加了约50天的提前时间。iPEB在独立的PLCO数据中改善了肺癌风险评估，支持目标驱动的纵向生物标志物优化用于早期检测。
英文摘要
Repeated blood-based biomarker measurements can improve cancer risk assessment by capturing longitudinal changes missed by single-time-point analyses. Parametric Empirical Bayes (PEB) incorporates prior measurements to estimate individualized reference values, but existing implementations do not account for the time between measurements and rely on predefined panels with fixed combination rules. We developed improved Parametric Empirical Bayes (iPEB), which accounts for the intervals between serial measurements, adjusts for covariates, and performs feature selection and optimized biomarker combination. iPEB optimizes biomarker weights for specific clinical objectives, such as maximizing sensitivity at a prespecified specificity or diagnostic lead time. We evaluated iPEB through simulations and a real-world application using six protein biomarkers (pro-SFTPB, CEA, CA125, CYFRA 21-1, osteopontin, and HE4) from a case-control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. The analysis included 324 lung cancer cases and 1,674 controls with at least two serial measurements; six centers were used for model development and four for independent validation. Optimized for sensitivity at 99% specificity, iPEB achieved 24.2% sensitivity in the independent test set, compared with 18.2% for conventional PEB applied to the same four-marker panel. Optimized instead for lead time, iPEB added approximately 50 days of lead time at that stringent operating point. iPEB improved lung cancer risk assessment in independent PLCO data, supporting objective-driven optimization of longitudinal biomarkers for early detection.
Comments32 pages, 3 figures, 3 tables. Submitted to Biometrics. Code and reproducibility materials: https://github.com/bitansa/iPEB
