Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay

作者：S. Xu, J. Luo, Wanxiangfu Tang, Hua Bao, Jia‐Yi Wang, Shuang Chang, Zihua Zou, Xuemo Fan, Yan‐Qun Liu, Chen Jiang, Xue Wu · 发表于：ESMO Open · 年份：2024 · DOI：10.1016/j.esmoop.2024.103595 · 被引用次数：13 · 研究领域：Lung Cancer Diagnosis and Treatment、Cancer Genomics and Diagnostics、Lung Cancer Treatments and Mutations

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...