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Research on Early Screening and Diagnosis of Pulmonary Nodules Based on Novel Non-invasive Technologies.
Sponsor: Chen KeZhong
Summary
This is a prospective observational study designed to address the clinical challenge posed by the high false-positive rate associated with CT imaging in early lung cancer screening. The primary objective is to develop a multi-omics technology for early lung cancer screening, leveraging \*\*exhaled breath metabolomics, plasma metabolomics, radiomics, and liquid biopsy. Based on large-sample detection data, the study aims to construct a \*\*multi-dimensional, sequential decision-making system\*\*. This system utilises the high accessibility of metabolomics for primary screening, combined with radiomics and ctDNA technologies for subsequent \*\*differentiation and definitive diagnosis. The research plans to prospectively enrol 300 patients with non-small cell lung cancer, along with corresponding subjects with benign nodules and healthy controls. By optimising the model using machine learning and deep learning algorithms (such as SVM, HRNet, and PAResNet), the ultimate goal is to establish a novel lung cancer early screening system characterised by \*\*high sensitivity, high accuracy, and high accessibility\*\*, enabling the precise differentiation and screening of healthy individuals, benign pulmonary nodules, and early-stage lung cancer.
Official title: Research on Precise Early Screening and Diagnosis of Pulmonary Nodules Based on a Novel Multidimensional Non-invasive Approach
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
1800
Start Date
2022-12-31
Completion Date
2026-06-30
Last Updated
2026-01-27
Healthy Volunteers
Yes
Conditions
Interventions
Employing multi-omics diagnostic approaches to enhance diagnostic efficacy
The study will first systematically evaluate the efficacy and accessibility of metabolomics and radiomics in the early screening and diagnosis of lung cancer through retrospective data analysis of prospective databases and prospective cohort validation. Based on large-scale detection data, a novel multidimensional early-stage lung cancer screening system will be established. This system will employ metabolomics as the initial screening method, supplemented by multi-omics approaches including radiomics, cfDNA methylation fragment detection, TCR detection, and metabolomics for differential diagnosis and confirmation.
Locations (1)
Peking University People's Hospital
Beijing, Beijing Municipality, China