Chen Pei-Hsing, Tsai Tung-Ming, Lu Tzu-Pin, Lu Hsiao-Hung, Pamart Dorian, Kotronoulas Aristotelis, Herzog Marielle, Micallef Jacob Vincent, Hsu Hsao-Hsun, Chen Jin-Shing
Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei City 106, Taiwan.
Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei City 100, Taiwan.
Cancers (Basel). 2025 Mar 7;17(6):916. doi: 10.3390/cancers17060916.
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.
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).
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.
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：迫切需要准确的非侵入性检测方法来改善肺癌的早期发现和诊断。然而，目前尚无经监管部门批准用于此目的的血液检测方法。我们旨在改进肺结节分类，以识别高患病率患者群体中的恶性结节。
本研究纳入了806名未确诊的、直径大于5毫米结节的参与者，重点评估循环血液中的核小体水平和组蛋白修饰（H3.1和H3K27Me3）。结节被分类为恶性或良性。为了建立模型，数据被随机分为训练数据集（n = 483）和验证数据集（n = 121）。然后使用单独的测试数据集（n = 202）评估模型的性能。
在患者中，755名（93.7%）有组织诊断。总体恶性率为80.4%。对于所有数据集，曲线下面积如下：训练集为0.74；验证集为0.86；测试集为0.79（准确率范围：0.80 - 0.88）。敏感性在所有数据集中结果一致（分别为0.91、0.95和0.93），而特异性范围为0.37至0.64。对于较小的结节（5 - 10毫米），模型记录的准确率值分别为0.76、0.88和0.85。敏感性值0.91、1.00和0.94进一步突出了该模型强大的诊断能力。该模型在报告和数据系统（RADS）类别中的表现显示出一致的准确性。
我们的表观遗传生物标志物组合在高危患者群体中早期检测非小细胞肺癌具有高敏感性和准确性。表观遗传生物标志物模型在识别高危肺结节方面特别有效，包括小的、部分实性和非实性结节，并为验证提供了进一步的证据。
