使用低剂量计算机断层扫描（LDCT）进行早期筛查可以降低非小细胞肺癌引起的死亡率。然而，约 25% LDCT 发现的“可疑”肺部结节后来通过切除手术被证实为良性，这增加了患者的不适和医疗保健系统的负担。在这项研究中，我们的目标是开发一种无创液体活检检测方法，利用游离 DNA (cfDNA) 片段组学分析区分肺部恶性肿瘤和良性但“可疑”的肺结节。一个独立的训练队列由 193 名恶性结节患者和 44 名患者组成良性结节被用来构建机器学习模型。使用四种不同片段组学概况的基本模型在堆叠到最终预测模型之前使用自动化机器学习方法进行了优化。使用一个独立验证队列（包括 96 个恶性结节和 22 个良性结节）和一个外部测试队列（包括 58 个恶性结节和 41 个良性结节）来评估堆叠集成模型的性能。我们的机器学习模型在检测方面表现出了出色的性能患有恶性结节的患者。独立验证队列和外部测试队列的曲线下面积分别达到 0.857 和 0.860。验证队列在 90% 的目标灵敏度 (89.6%) 下实现了出色的特异性 (68.2%)。将截止值应用于外部队列时，观察到了同样良好的性能，其特异性达到 63.4%，敏感性为 89.7%。独立验证队列的亚组分析显示，检测结节大小（<1 cm：91.7%；1-3 cm：88.1%；>3 cm：100%；未知：100%）和吸烟史的各个亚组的敏感性（是：88.2%；否：89.9%）在肺癌组中均保持较高水平。我们的 cfDNA 片段组学检测可以提供一种非侵入性方法来区分恶性结节与放射学可疑但病理学良性的结节，从而修正 LDCT 假阳性。版权所有 © 2024作者。由爱思唯尔有限公司出版。保留所有权利。

Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet 'suspicious' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling.An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model.Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group.Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.