Background/Objectives: Accurate non-invasive tests to improve early detection and diagnosis of lung cancer are urgently needed. However, no regulatory-approved blood tests are available for this purpose. We aimed to improve pulmonary nodule classification to identify malignant nodules in a high-prevalence patient group. Methods: This study involved 806 participants with undiagnosed nodules larger than 5 mm, focusing on assessing nucleosome levels and histone modifications (H3.1 and H3K27Me3) in circulating blood. Nodules were classified as malignant or benign. For model development, the data were randomly divided into training (n = 483) and validation (n = 121) datasets. The model’s performance was then evaluated using a separate testing dataset (n = 202). Results: Among the patients, 755 (93.7%) had a tissue diagnosis. The overall malignancy rate was 80.4%. For all datasets, the areas under curves were as follows: training, 0.74; validation, 0.86; and test, 0.79 (accuracy range: 0.80–0.88). Sensitivity showed consistent results across all datasets (0.91, 0.95, and 0.93, respectively), whereas specificity ranged from 0.37 to 0.64. For smaller nodules (5–10 mm), the model recorded accuracy values of 0.76, 0.88, and 0.85. The sensitivity values of 0.91, 1.00, and 0.94 further highlight the robust diagnostic capability of the model. The performance of the model across the reporting and data system (RADS) categories demonstrated consistent accuracy. Conclusions: Our epigenetic biomarker panel detected non-small-cell lung cancer early in a high-risk patient group with high sensitivity and accuracy. The epigenetic biomarker model was particularly effective in identifying high-risk lung nodules, including small, part-solid, and non-solid nodules, and provided further evidence for validation.
背景/目的：迫切需要开发准确的非侵入性检测方法以提升肺癌的早期发现与诊断水平。然而，目前尚无监管机构批准用于此目的的血检方法。本研究旨在优化肺结节分类模型，以在高患病风险人群中识别恶性结节。方法：本研究纳入806名未确诊且结节直径大于5毫米的参与者，重点评估其循环血液中的核小体水平及组蛋白修饰（H3.1与H3K27Me3）。结节被分类为恶性或良性。在模型开发阶段，数据被随机划分为训练集（n=483）和验证集（n=121），随后使用独立测试集（n=202）评估模型性能。结果：在全部患者中，755名（93.7%）获得组织学诊断。总体恶性率为80.4%。所有数据集的曲线下面积分别为：训练集0.74、验证集0.86、测试集0.79（准确率范围0.80-0.88）。灵敏度在所有数据集中表现一致（分别为0.91、0.95、0.93），而特异度范围在0.37-0.64之间。针对小结节（5-10毫米），模型准确率分别为0.76、0.88、0.85，灵敏度值0.91、1.00、0.94进一步凸显了模型的稳健诊断能力。模型在影像报告与数据系统（RADS）各分类中均表现出稳定的准确性。结论：我们的表观遗传生物标志物组合在高风险人群中实现了对非小细胞肺癌的高灵敏度与高准确度早期检测。该表观遗传生物标志物模型在识别高风险肺结节（包括小结节、部分实性及非实性结节）方面表现尤为突出，并为验证提供了进一步证据。
Accurate Diagnosis of High-Risk Pulmonary Nodules Using a Non-Invasive Epigenetic Biomarker Test
肿瘤(癌症)患者之家