The Department of Thoracic Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
The Department of Thoracic Surgery, The First Hospital of China Medical University, Shenyang, Liaoning, China.
ESMO Open. 2024 Aug;9(8):103595. doi: 10.1016/j.esmoop.2024.103595. Epub 2024 Jul 31.
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.
低剂量计算机断层扫描（LDCT）早期筛查可降低非小细胞肺癌导致的死亡率。然而，通过 LDCT 确定的“疑似”肺结节中，约有 25%在通过切除手术证实为良性，这给患者带来了不适，并增加了医疗系统的负担。在这项研究中，我们旨在开发一种非侵入性液体活检检测方法，通过细胞游离 DNA（cfDNA）片段组学分析来区分肺部恶性肿瘤与良性但“疑似”肺结节。
使用 193 例恶性结节患者和 44 例良性结节患者的独立训练队列构建机器学习模型。使用自动化机器学习方法优化了基于四种不同片段组学特征的基础模型，然后将其堆叠到最终预测模型中。使用包括 96 例恶性结节和 22 例良性结节的独立验证队列和包括 58 例恶性结节和 41 例良性结节的外部测试队列来评估堆叠集成模型的性能。
我们的机器学习模型在检测恶性结节患者方面表现出色。在独立验证队列和外部测试队列中，曲线下面积分别达到 0.857 和 0.860。验证队列在靶向 90%灵敏度（89.6%）时达到了优异的特异性（68.2%）。在应用于外部队列时，观察到了同样良好的性能，特异性为 63.4%，灵敏度为 89.7%。对独立验证队列的亚组分析表明，在肺癌组中，检测各种大小结节（<1cm：91.7%；1-3cm：88.1%；>3cm：100%；未知：100%）和吸烟史（是：88.2%；否：89.9%）的敏感性仍然很高。
我们的 cfDNA 片段组学检测方法可以提供一种非侵入性方法，区分恶性结节与影像学可疑但病理良性的结节，纠正 LDCT 的假阳性。
