A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules

Accurate, non-invasive, and cost-effective tools are needed to assist pulmonary nodule diagnosis and management due to increasing detection by low-dose computed tomography (LDCT). We perform genome-wide methylation sequencing on malignant and non-malignant lung tissues and designed a panel of 263 differential DNA methylation regions, which is used for targeted methylation sequencing on blood cell-free DNA (cfDNA) in two prospectively collected and retrospectively analyzed multicenter cohorts. We develop and optimize an integrative model for risk stratification of pulmonary nodules based on 40 cfDNA methylation biomarkers, age, and five simple computed tomography (CT) imaging features using machine learning approaches and validate its good performance in two cohorts. Using the two-threshold strategy can effectively reduce unnecessary invasive surgeries, overtreatment costs, and injury for patients with benign nodules while advising immediate treatment for patients with lung cancer, which can potentially improve the overall diagnosis of lung cancer following LDCT/CT screening.

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Publication Details

Journal
Cell Reports Medicine
Published
2024-09-27
DOI
https://doi.org/10.1016/j.xcrm.2024.101750
Citations
10
Primary Topic
Lung Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
4.19

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article

A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules

Shuangxiu Wu, Wenhua Liang, Jian‐Bing Fan, Chao Cheng et al.
10 citations
Cell Reports Medicine
Lung Cancer Diagnosis and Treatment
4.19
article

A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules

Shuangxiu Wu, Wenhua Liang, Jian‐Bing Fan, Chao Cheng, Zhujia Ye, Qunqing Chen, Jinsheng Tao, Lunxu Liu, Hui Tian, Teng Lin, Nanshan Zhong, Yubiao Guo, Dan Liu, Haitao Sun, Jianxing He, Jiaqing Zhang
article en
10 citations

Abstract

Accurate, non-invasive, and cost-effective tools are needed to assist pulmonary nodule diagnosis and management due to increasing detection by low-dose computed tomography (LDCT). We perform genome-wide methylation sequencing on malignant and non-malignant lung tissues and designed a panel of 263 differential DNA methylation regions, which is used for targeted methylation sequencing on blood cell-free DNA (cfDNA) in two prospectively collected and retrospectively analyzed multicenter cohorts. We develop and optimize an integrative model for risk stratification of pulmonary nodules based on 40 cfDNA methylation biomarkers, age, and five simple computed tomography (CT) imaging features using machine learning approaches and validate its good performance in two cohorts. Using the two-threshold strategy can effectively reduce unnecessary invasive surgeries, overtreatment costs, and injury for patients with benign nodules while advising immediate treatment for patients with lung cancer, which can potentially improve the overall diagnosis of lung cancer following LDCT/CT screening.

Cell Reports MedicineVol. 5(10)
Sun Yat-sen University (CN), Sichuan University (CN), West China Hospital of Sichuan University (CN), First Affiliated Hospital of Guangzhou Medical University (CN), Rex Medical (United States) (US), Zhujiang Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Qilu Hospital of Shandong University (CN), Southern Medical University (CN), Guangzhou Medical University (CN)
National Natural Science Foundation of China, Sichuan University, West China Hospital, Sichuan University
Zero hunger
Openalex Percentile: Top 5%
Lung Cancer Diagnosis and Treatment
4.19
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