PDF(Pubmed)
Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.
摘要：
背景：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,LDCT发现的可疑肺结节中有25%后来通过切除手术证实为良性，增加患者的不适和医疗系统的负担。在这项研究中,我们的目标是使用无细胞DNA(cfDNA)片段组学分析,开发一种非侵入性液体活检方法,用于区分肺部恶性肿瘤和良性但可疑的肺结节.
方法：使用由193例恶性结节患者和44例良性结节患者组成的独立训练队列来构建机器学习模型。使用四个不同碎片组学概况的基础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。一个独立的验证队列，其中恶性结节96个,良性结节22个,和一个外部测试队列，包括58个恶性结节和41个良性结节，用于评估堆叠集成模型的性能。
结果：我们的机器学习模型在检测恶性结节患者方面表现出优异的性能。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,分别。验证队列在靶向90%灵敏度(89.6%)下实现了优异的特异性(68.2%)。在将截止值应用于外部队列时，观察到了相当好的表现，特异性达到63.4%，灵敏度为89.7%。独立验证队列的亚组分析显示，在肺癌组中，检测结节大小的各个亚组（<1cm：91.7％；1-3cm：88.1％；>3cm：100％；未知：100％）和吸烟史（是：88.2％；否：89.9％）的敏感性均保持较高。
结论：我们的cfDNA片段组学分析可以提供一种非侵入性方法来区分恶性结节和影像学可疑但病理良性结节，修改LDCT误报。
方法：使用由193例恶性结节患者和44例良性结节患者组成的独立训练队列来构建机器学习模型。使用四个不同碎片组学概况的基础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。一个独立的验证队列，其中恶性结节96个,良性结节22个,和一个外部测试队列，包括58个恶性结节和41个良性结节，用于评估堆叠集成模型的性能。
结果：我们的机器学习模型在检测恶性结节患者方面表现出优异的性能。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,分别。验证队列在靶向90%灵敏度(89.6%)下实现了优异的特异性(68.2%)。在将截止值应用于外部队列时，观察到了相当好的表现，特异性达到63.4%，灵敏度为89.7%。独立验证队列的亚组分析显示，在肺癌组中，检测结节大小的各个亚组（<1cm：91.7％；1-3cm：88.1％；>3cm：100％；未知：100％）和吸烟史（是：88.2％；否：89.9％）的敏感性均保持较高。
结论：我们的cfDNA片段组学分析可以提供一种非侵入性方法来区分恶性结节和影像学可疑但病理良性结节，修改LDCT误报。
