Lung CancerNCT07370077
Research on Early Screening and Diagnosis of Pulmonary Nodules Based on Novel Non-invasive Technologies.
An observational study of Lung Cancer, run by Chen KeZhong at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer
- What is being tested?
- Employing multi-omics diagnostic approaches to enhance diagnostic efficacy
- How many people
- 1,800 participants
- Where
- 1 siteChina
- Study dates
- 31 Dec 2022to 30 Jun 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Peking University People's Hospital
Beijing, Beijing Municipality, 10010
Kezhong Chen, 86-13488752289, chenkezhong@pkuph.edu.cn
About this study
Official title: Research on Precise Early Screening and Diagnosis of Pulmonary Nodules Based on a Novel Multidimensional Non-invasive Approach
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.
Read the detailed description
Lung cancer is one of the malignant tumours with a poor prognosis. Data from 61 countries globally show that the 5-year survival rate for lung cancer patients is only 10.0% to 20.0%. Since 2005, China has launched several major national public health service projects, including lung cancer screening initiatives such as the Rural Cancer Early Diagnosis and Treatment Program and the Urban Cancer Early Diagnosis and Treatment Program, gradually establishing a national network for lung cancer screening and early diagnosis/treatment. Although China has made significant progress in the diagnosis and treatment of lung cancer, the 5-year survival rate for the Chinese population was 19.7% between 2012 and 2015. The prognosis for lung cancer remains poor, making early diagnosis and treatment an essential guide for our research to improve patient survival.
CT scanning has become the recognised standard tool for lung cancer screening and early diagnosis. However, the false-positive rate for highly suspected nodules remains high, at 30% to 40%, and the accuracy of preoperative lesion assessment needs improvement. Therefore, establishing a highly sensitive, highly specific, minimally invasive, or even non-invasive, precise diagnostic method for early lung cancer, thereby avoiding unnecessary surgery, is a critical clinical problem urgently needing resolution, and is of great significance for advancing China's level of early lung cancer diagnosis.
This study will first systematically evaluate the efficacy and accessibility of **exhaled breath metabolomics, plasma metabolomics, and radiomics in early lung cancer screening and diagnosis using a prospective database. Based on large-sample detection data, we will construct a novel multi-dimensional stereo lung cancer early screening system. This system uses exhaled breath metabolomics and plasma metabolomics for initial screening. Then it integrates multiple omics technologies, including radiomics, cfDNA methylation and fragmentomics, and the original metabolomics data, for differentiation and definitive diagnosis. Modelling algorithms will be optimised using methods such as cross-validation, internal validation, and stratified validation to form a stable lung cancer screening system.
- Conditions
- Lung Cancer
- Being tested
- Employing multi-omics diagnostic approaches to enhance diagnostic efficacy
- Keywords
- lung cancer; Early Diagnosis；Metabolomics；Genomics
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Employing multi-omics diagnostic approaches to enhance diagnostic efficacyDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Healthy, Benign, Malignant | Incorporating healthy individuals, benign nodules, and malignant nodules into the study population to reflect real-world screening scenarios. Receives: Diagnostic Test: Employing multi-omics diagnostic approaches to enhance diagnostic efficacy |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: From enrollment to the end of treatment at 6-8 weeks
Secondary outcomes (1)
Positive predictive value; negative predictive value and et al.
Supplementary assessments of model performance were conducted across multiple dimensions. Regarding supplementary diagnostic efficacy metrics, the positive predictive value reflects the proportion of subjects with positive screening results who genuinely have lung cancer, a crucial indicator for evaluating the clinical utility of screening strategies. The negative predictive value assesses the reliability of excluding lung cancer when screening results are negative, providing significant reference value for reducing the risk of missed diagnoses. Positive likelihood ratios and negative likelihood ratios, as diagnostic performance metrics unaffected by prevalence, provide a more objective reflection of the model's diagnostic value. The Youden index, as a composite measure of sensitivity and specificity, is employed to determine the optimal diagnostic threshold.
Measured: 35 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Jan 2026
- Study start31 Dec 2022
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,800 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chen KeZhong
- Sponsor type
- University, hospital or other
- Collaborators
- Tangshan Worker's Hospital, School of Medical Science and Engineering, Beihang University, China Aerospace Science and Industry Corporation No. 731 Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Rongcheng County People's Hospital
- Responsible party
- Sponsor-investigator, Chen KeZhong, Peking University People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07370077
- Sponsor's protocol ID
- 2023XAGG0071
- Status last verified
- Jan 2026
- First submitted
- 19 Jan 2026
- Last updated
- 27 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Precise Early Screening and Diagnosis of Pulmonary Nodules Based on a Novel Multidimensional Non-invasive Approach
- ClinicalTrials.gov ID
- NCT07370077
- Acronym / sponsor ID
- 2023XAGG0071
- Conditions
- Lung Cancer
- Keywords
- lung cancer; Early Diagnosis；Metabolomics；Genomics
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,800 participants (estimated)
- Lead sponsor
- Chen KeZhongUniversity, hospital or other
- Collaborators
- Tangshan Worker's Hospital, School of Medical Science and Engineering, Beihang University, China Aerospace Science and Industry Corporation No. 731 Hospital, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Rongcheng County People's Hospital
- Study start
- 31 Dec 2022 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 27 Jan 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Lung cancer is one of the malignant tumours with a poor prognosis. Data from 61 countries globally show that the 5-year survival rate for lung cancer patients is only 10.0% to 20.0%. Since 2005, China has launched several major national public health service projects, including lung cancer screening initiatives such as the Rural Cancer Early Diagnosis and Treatment Program and the Urban Cancer Early Diagnosis and Treatment Program, gradually establishing a national network for lung cancer screening and early diagnosis/treatment. Although China has made significant progress in the diagnosis and treatment of lung cancer, the 5-year survival rate for the Chinese population was 19.7% between 2012 and 2015. The prognosis for lung cancer remains poor, making early diagnosis and treatment an essential guide for our research to improve patient survival.
CT scanning has become the recognised standard tool for lung cancer screening and early diagnosis. However, the false-positive rate for highly suspected nodules remains high, at 30% to 40%, and the accuracy of preoperative lesion assessment needs improvement. Therefore, establishing a highly sensitive, highly specific, minimally invasive, or even non-invasive, precise diagnostic method for early lung cancer, thereby avoiding unnecessary surgery, is a critical clinical problem urgently needing resolution, and is of great significance for advancing China's level of early lung cancer diagnosis.
This study will first systematically evaluate the efficacy and accessibility of **exhaled breath metabolomics, plasma metabolomics, and radiomics in early lung cancer screening and diagnosis using a prospective database. Based on large-sample detection data, we will construct a novel multi-dimensional stereo lung cancer early screening system. This system uses exhaled breath metabolomics and plasma metabolomics for initial screening. Then it integrates multiple omics technologies, including radiomics, cfDNA methylation and fragmentomics, and the original metabolomics data, for differentiation and definitive diagnosis. Modelling algorithms will be optimised using methods such as cross-validation, internal validation, and stratified validation to form a stable lung cancer screening system.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Healthy, Benign, Malignant | – | Diagnostic Test: Employing multi-omics diagnostic approaches to enhance diagnostic efficacyIncorporating healthy individuals, benign nodules, and malignant nodules into the study population to reflect real-world screening scenarios. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (7 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Employing multi-omics diagnostic approaches to enhance diagnostic efficacyThe 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. | Diagnostic test | Healthy, Benign, Malignant |
Primary outcome
diagnostic sensitivity
Time frame: From enrollment to the end of treatment at 6-8 weeks
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
1 secondary outcome
Positive predictive value; negative predictive value and et al.
Time frame: 35 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Jan 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer, run by Chen KeZhong at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 27 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Fan Yang, Chief Physician (0086-88326657, yangfan@pkuph.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
diagnostic sensitivity (time frame: From enrollment to the end of treatment at 6-8 weeks). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Jan 2026.