Research on Early Screening and Diagnosis of Pulmonary Nodules Based on Novel Non-invasive Technologies.

NCT07370077 | Recruiting

• 1 other identifier: 2023XAGG0071
• Study Type: observational
• Target: 1,800
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Lung Cancer

Keywords

lung cancer; Early Diagnosis；Metabolomics；Genomics

Outcome Measures

Primary Outcomes (1)

• diagnostic sensitivity

  The primary research indicators in this study focus on evaluating the diagnostic efficacy of multi-omics models for early-stage lung cancer. Firstly, diagnostic sensitivity serves as the core metric to assess the model's ability to correctly identify lung cancer patients, with a target value set at no less than 85%. Diagnostic specificity measures the model's capacity to correctly exclude non-lung cancer individuals, with a target value set at no less than 90%. The area under the receiver operating characteristic curve serves as a comprehensive indicator of the model's discriminative capability, with a target value exceeding 0.90 to ensure robust overall diagnostic performance. Regarding early detection capability, the detection rate for stage I lung cancer represents a key primary indicator in this study, specifically encompassing the detection of stage IA and IB lung cancer. This is because patients at this stage typically present with the optimal surgical resection opportunities an

  From enrollment to the end of treatment at 6-8 weeks

Secondary Outcomes (1)

• Positive predictive value; negative predictive value and et al.

  35 months

Study Arms (1)

Healthy, Benign, Malignant

Incorporating healthy individuals, benign nodules, and malignant nodules into the study population to reflect real-world screening scenarios.

Diagnostic Test: Employing multi-omics diagnostic approaches to enhance diagnostic efficacy

Interventions

Employing multi-omics diagnostic approaches to enhance diagnostic efficacy DIAGNOSTIC_TEST

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.

Healthy, Benign, Malignant

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

This study proposes to prospectively recruit participants with early-stage non-small cell lung cancer, benign pulmonary lesions (including tuberculosis, hamartomas, inflammatory conditions, etc.), and healthy individuals undergoing medical examinations at Peking University People's Hospital, Rongcheng County People's Hospital in Xiong'an, Tangshan Workers' Hospital, Union Hospital affiliated with Tongji Medical College of Huazhong University of Science and Technology, and the 731 Hospital of China Aerospace Science and Industry Corporation.

You may qualify if:

• Age \>18 years old.
• Availability of both exhaled breath and peripheral blood samples, and raw CT image data; the collection time is within one month before biopsy or surgical resection, and the subject has not received any treatment in between.
• Pulmonary nodular lesions identified by chest CT with a diameter \< 3 cm.
• Pulmonary nodular lesions must be surgically resected and have complete, definitive pathological information regarding their benign or malignant nature.
• No prior history of malignant tumors.
• Has not received anti-tumor treatments such as radiotherapy, chemotherapy, or targeted therapy.
• Signed informed consent.

You may not qualify if:

• Missing clinical data or incomplete sample collection.
• Presence or suspicion of active infection or other severe co-morbidities.
• Abnormal liver or kidney function.
• Indefinite or inconclusive postoperative pathological results.

Sponsors & Collaborators

• Tangshan Workers' Hospital: collaborator
• China Aerospace Science and Industry Corporation No. 731 Hospital: collaborator
• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: collaborator
• Rongcheng County People's Hospital: collaborator
• Chen KeZhong: lead
• School of Medical Science and Engineering, Beihang University: collaborator

Study Sites (1)

Peking University People's Hospital

Beijing, Beijing Municipality, 10010, China

RECRUITING

Biospecimen

Retention: SAMPLES WITH DNA

blood

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Fan Yang, Chief Physician

CONTACT

0086-88326657; yangfan@pkuph.edu.cn

zhong Ke Chen

CONTACT

+86-010-88325983; mdkzchen@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: January 19, 2026
• First Posted: January 27, 2026
• Study Start: December 31, 2022
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): June 30, 2026
• Last Updated: January 27, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)