Objective: Although low-dose computed tomography has been proven effective to reduce lung cancer-specific mortality, a considerable proportion of surgically resected high-risk lung nodules were still confirmed pathologically benign. There is an unmet need of a novel method for malignancy classification in lung nodules.
Methods: We recruited 307 patients with high-risk lung nodules who underwent curative surgery, and 247 and 60 cases were pathologically confirmed malignant and benign lung lesions, respectively. Plasma samples from each patient were collected before surgery and performed low-depth (5×) whole-genome sequencing. We extracted cell-free DNA (cfDNA) characteristics and determined radiomic features. We built models to classify the malignancy using our data and further validated models with two independent lung nodule cohorts.
Results: Our models using one type of profile were able to distinguish lung cancer and benign lung nodules (BLNs) at an area under the curve (AUC) metrics of 0.69-0.91 in the study cohort. Integrating all the five base models using cfDNA profiles, the cfDNA-based ensemble model achieved an AUC of 0.95 (95%CI: 0.92-0.97) in the study cohort and 0.98 (95%CI: 0.96-1.00) in the validation cohort. At a specificity of 95.0%, the sensitivity reached 80.0% in the study cohort. With the same threshold, the specificity and sensitivity had similar performances in both validation cohorts. Furthermore, the performance of AUC reached 0.97 both in the study and validation cohorts when considering the radiomic profile.
Conclusions: The cfDNA profiles-based method is an efficient non-invasive tool to distinguish malignancies and high-risk but pathologically BLNs.
Keywords: Cell-Free DNA fragmentomics; Liquid biopsy; Lung cancer; Radiomics; Whole-genome sequencing.
Copyright © 2024. Published by Elsevier Inc.