Background/Objectives: Lung cancer screening can reduce patient mortality. Multiple issues persist including timely management of patients with a radiologically defined indeterminate pulmonary nodule (IPN), which carries unknown pathological significance. This pilot study focused on combining demographic, clinical, radiographic, and common circulating biomarkers for their ability to aid in IPN risk of malignancy prediction. Methods: A case-control cohort consisting of 379 patients with IPNs (251 stage I lung tumors and 128 nonmalignant nodules) was used for this effort, divided into training (70%) and testing (30%) sets. Demographic variables (age, sex, race, ethnicity), radiographic information (nodule size and location), smoking pack-years, and plasma biomarker levels of CA-125, SCC, CEA, HE4, ProGRP, NSE, Cyfra 21-1, IL-6, PlGF, sFlt-1, hs-CRP, Ferritin, IgG, IgE, IgM, IgA, and Kappa and Lambda Free Light Chains were assessed for this purpose. Results: Multivariable analyses of biomarker, demographic, and radiographic variables yielded a model consisting of age, lesion size, pack-years, history of extrathoracic cancer, upper lobe location, spiculation, hs-CRP, NSE, Ferritin, and CA-125 (AUC = 0.872 in training, 0.842 in testing) with superior performance over the Mayo Score model, which consists of age, lesion size, history of smoking, history of extrathoracic cancer, upper lobe location, and spiculation (AUC = 0.816 in training, 0.787 in testing). Conclusions: In conclusion, a simple reduced algorithm consisting of biomarkers, clinical information, and demographic variables may have value for malignancy prediction of screen-detected IPNs. Upon further validation, this method stands to reduce the need for serial radiographic studies and the risks of diagnostic delay.
背景/目的：肺癌筛查可降低患者死亡率，但诸多问题依然存在，包括对影像学定义的不确定肺结节（IPN）的及时管理，此类结节具有未知的病理学意义。本项初步研究旨在结合人口统计学、临床、影像学及常见循环生物标志物，评估其在辅助预测IPN恶性风险方面的能力。方法：本研究采用包含379例IPN患者（251例I期肺肿瘤和128例非恶性结节）的病例对照队列，分为训练集（70%）和测试集（30%）。评估了人口统计学变量（年龄、性别、种族、民族）、影像学信息（结节大小和位置）、吸烟包年数，以及血浆生物标志物水平，包括CA-125、SCC、CEA、HE4、ProGRP、NSE、Cyfra 21-1、IL-6、PlGF、sFlt-1、hs-CRP、铁蛋白、IgG、IgE、IgM、IgA以及κ和λ游离轻链。结果：对生物标志物、人口统计学和影像学变量的多变量分析得出一个模型，包含年龄、病灶大小、吸烟包年数、胸外癌症史、上叶位置、毛刺征、hs-CRP、NSE、铁蛋白和CA-125（训练集AUC = 0.872，测试集AUC = 0.842），其性能优于梅奥评分模型（包含年龄、病灶大小、吸烟史、胸外癌症史、上叶位置和毛刺征；训练集AUC = 0.816，测试集AUC = 0.787）。结论：总之，一个由生物标志物、临床信息和人口统计学变量构成的简化算法可能对筛查发现的IPN的恶性预测具有价值。经过进一步验证，该方法有望减少连续影像学检查的需求，并降低诊断延迟的风险。
Exploratory Algorithms to Aid in Risk of Malignancy Prediction for Indeterminate Pulmonary Nodules
肿瘤(癌症)患者之家