Department of Respiratory Endoscopy, Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Engineering Research Center of Respiratory Endoscopy, Shanghai, China.
Department of Pathology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Lung Cancer. 2024 Sep;195:107930. doi: 10.1016/j.lungcan.2024.107930. Epub 2024 Aug 11.
With the popularization of computed tomography, more and more pulmonary nodules (PNs) are being detected. Risk stratification of PNs is essential for detecting early-stage lung cancer while minimizing the overdiagnosis of benign nodules. This study aimed to develop a circulating tumor DNA (ctDNA) methylation-based, non-invasive model for the risk stratification of PNs.
A blood-based assay ("LUNG-TRAC") was designed to include novel lung cancer ctDNA methylation markers identified from in-house reduced representative bisulfite sequencing data and known markers from the literature. A stratification model was trained based on 183 ctDNA samples derived from patients with benign or malignant PNs and validated in 62 patients. LUNG-TRAC was further single-blindly tested in a single- and multi-center cohort.
The LUNG-TRAC model achieved an area under the curve (AUC) of 0.810 (sensitivity = 74.4 % and specificity = 73.7 %) in the validation set. Two test sets were used to evaluate the performance of LUNG-TRAC, with an AUC of 0.815 in the single-center test (N = 61; sensitivity = 67.5 % and specificity = 76.2 %) and 0.761 in the multi-center test (N = 95; sensitivity = 50.7 % and specificity = 80.8 %). The clinical utility of LUNG-TRAC was further assessed by comparing it to two established risk stratification models: the Mayo Clinic and Veteran Administration models. It outperformed both in the validation and the single-center test sets.
The LUNG-TRAC model demonstrated accuracy and consistency in stratifying PNs for the risk of malignancy, suggesting its utility as a non-invasive diagnostic aid for early-stage peripheral lung cancer.
www.
gov (NCT03989219).
随着计算机断层扫描的普及，越来越多的肺结节（PNs）被检测到。对 PNs 进行风险分层对于检测早期肺癌至关重要，同时最大限度地减少良性结节的过度诊断。本研究旨在开发一种基于循环肿瘤 DNA（ctDNA）甲基化的非侵入性模型，用于 PNs 的风险分层。
设计了一种基于血液的检测方法（“LUNG-TRAC”），该方法包括从内部代表性亚硫酸氢盐测序数据中鉴定出的新型肺癌 ctDNA 甲基化标志物和文献中已知的标志物。基于来自良性或恶性 PNs 患者的 183 个 ctDNA 样本，训练了一个分层模型，并在 62 名患者中进行了验证。LUNG-TRAC 进一步在单中心和多中心队列中进行了单盲测试。
LUNG-TRAC 模型在验证集中的曲线下面积（AUC）为 0.810（灵敏度= 74.4%，特异性= 73.7%）。两个测试集用于评估 LUNG-TRAC 的性能，单中心测试的 AUC 为 0.815（N= 61；灵敏度= 67.5%，特异性= 76.2%），多中心测试的 AUC 为 0.761（N= 95；灵敏度= 50.7%，特异性= 80.8%）。通过将 LUNG-TRAC 与两种已建立的风险分层模型（梅奥诊所和退伍军人管理局模型）进行比较，进一步评估了 LUNG-TRAC 的临床实用性。它在验证集和单中心测试集均优于这两种模型。
LUNG-TRAC 模型在对 PNs 进行恶性风险分层方面表现出准确性和一致性，表明其可用作早期周围性肺癌的非侵入性诊断辅助工具。
www.
gov（NCT03989219）。
