Early-Stage Lung CancerUPD-LCNCT06733311
A Study Developing a Non-invasive Urine-based Proteomic Model for Early Lung Cancer Detection.
An observational study of Early-Stage Lung Cancer and NSCLC, run by Beijing Chao Yang Hospital at 1 site in China.
Trial summary
Brief Summary: The goal of this observational study is to develop a non-invasive urine proteomic diagnostic model to improve early-stage lung cancer detection. The study aims to answer the following main questions: Can urine proteomics reliably differentiate early-stage lung cancer from benign conditions? How does the… Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Early-Stage Lung Cancer, NSCLC, Pulmonary Nodule
- How many people
- 480 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2024to 31 Dec 2024
- How long will I take part?
- 2 Years
- Phase
- Observational
- Are there results?
- None posted in the registry
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 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population includes patients suspected of having early-stage (I-IIIA, non-N2) non-small cell lung cancer (NSCLC), recruited from the thoracic surgery and respiratory departments of Beijing Chao-Yang Hospital and collaborating clinical centers. Participants are individuals scheduled for surgical intervention based on preoperative clinical and imaging assessments.
Who may be able to join
- Male or female participants aged 18 to 75 years.
- Diagnosed or highly suspected early-stage (I-IIIA, non-N2) non-small cell lung 3.cancer (NSCLC) based on imaging or clinical assessment.
- 4.No prior anti-cancer treatment, including surgery, chemotherapy, radiotherapy, targeted therapy, or immunotherapy.
- 5.Able to provide informed consent and willing to comply with the study protocol, including urine sample collection before surgery.
- 6.Diagnosis confirmed within 42 days post-imaging or preoperative assessment through biopsy or surgical specimen.
Who cannot join
- History of any cancer treatment prior to study enrollment.
- Presence of metastatic disease (N2 or more advanced staging).
- Severe comorbid conditions or organ dysfunctions (e.g., renal failure) that could affect urine sample quality or interpretation.
- Pregnancy or lactation.
- Participation in another clinical study that could interfere with the outcomes of this study.
- Inability to comply with the study protocol, including language barriers or cognitive impairments.
Trial locations and contacts
1 site in 1 country.
Bin Hu, MD contact
China1 site, 1 recruiting
- Recruiting
Beijing Chao-Yang Hospital, Capital Medical University
Chaoyang District, Beijing Municipality, 100000
Bin Hu, MD, +86 139-0130-1750, cyyyhubin@163.com
Fanjie Meng, MD, +86 188-2266-5731, mfjwill99@gmail.com
About this study
Official title: Urine Proteomic Precision Diagnosis Model for Early Stage Lung Cancer
Brief Summary:
The goal of this observational study is to develop a non-invasive urine proteomic diagnostic model to improve early-stage lung cancer detection. The study aims to answer the following main questions:
Can urine proteomics reliably differentiate early-stage lung cancer from benign conditions? How does the diagnostic model compare to current clinical and imaging methods in accuracy?
Participants will:
Provide preoperative urine samples. Undergo proteomic analysis of urine samples. Have clinical, imaging, and proteomic data integrated into an AI-assisted diagnostic model.
The study will evaluate the sensitivity and specificity of this innovative diagnostic approach.
Read the detailed description
Detailed Description:
This study focuses on developing a urine proteomic-based diagnostic model to improve the early detection of lung cancer. It leverages non-invasive urine sampling, proteomic analysis, and artificial intelligence to create a high-sensitivity, high-specificity diagnostic tool.
The study will recruit 480 participants with suspected early-stage lung cancer (I-IIIA, non-N2). Urine samples will be collected before surgery, and participants will undergo standard imaging and diagnostic evaluations, including chest CT, abdominal ultrasound or CT, brain MRI or CT, and bone scans.
The primary objectives of the study include:
- Biomarker Identification: Identifying differentially expressed urine proteins associated with early-stage lung cancer.
- Diagnostic Model Construction: Combining proteomic findings with clinical and imaging data to construct a diagnostic model using AI-based algorithms.
- Validation: Evaluating the model's diagnostic accuracy compared to current clinical practices.
Participants will contribute to the advancement of a novel diagnostic method that aims to minimize unnecessary invasive procedures and improve lung cancer prognosis through early and accurate detection.
- Conditions
- Early-Stage Lung Cancer, NSCLC, Pulmonary Nodule
- Keywords
- Early-Stage Lung Cancer, Pulmonary Nodule, Urine Proteomics, Non-Invasive Diagnosis, Artificial Intelligence
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Urine Proteomics Diagnostic Group | Participants in this group will undergo urine proteomic analysis before surgery to predict early-stage non-small cell lung cancer (NSCLC). The predictions include tumor histopathological subtypes, lymph node metastasis, and other pathological factors. The accuracy of the diagnostic model will be compared to pathological results after surgery. This group consists of approximately 240 participants, with an anticipated 10% loss accounted for. | |
| CT Diagnostic Group | Participants in this group will undergo standard preoperative chest CT imaging to predict early-stage non-small cell lung cancer (NSCLC). Predictions include tumor histopathological subtypes, lymph node metastasis, and other pathological factors. The accuracy of the imaging predictions will be compared to pathological results after surgery. This group also consists of approximately 240 participants, with an anticipated 10% loss accounted for. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction Accuracy of Diagnostic Models
The primary outcome measure is the accuracy of preoperative predictions (sensitivity and specificity) for early-stage non-small cell lung cancer (NSCLC) diagnosis. Predictions are based on:
- Urine proteomics in the experimental group.
- Chest CT imaging in the control group.
Accuracy will be assessed by comparing preoperative predictions with postoperative pathological findings, including tumor histopathological subtypes, lymph node metastasis, and other pathological factors.
Measured: Within 2 weeks post-surgery.
Secondary outcomes (3)
Cut-off Value for Urine Proteomics Diagnostic Test
Determination of the optimal cut-off value for urine proteomic markers to maximize diagnostic sensitivity and specificity for early-stage non-small cell lung cancer (NSCLC).
Measured: Within 1 month after data analysis.
Comparative Performance of Diagnostic Models
Evaluation of the diagnostic performance (sensitivity, specificity, and area under the curve [AUC]) of the urine proteomic model versus chest CT imaging for predicting tumor histopathological subtypes, lymph node metastasis, and staging.
Measured: Within 2 months post-surgery.
Long-term Diagnostic Effectiveness
Evaluation of the correlation between preoperative diagnostic accuracy and 2-year postoperative clinical outcomes (e.g., recurrence rates, survival outcomes).
Measured: Up to 2 years post-surgery.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Dec 2024
- Study start1 Mar 2024
- Primary completion31 Dec 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Early-Stage 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
- 480 participants (estimated)
- Target duration
- 2 Years
Sponsor and investigators
- Lead sponsor
- Beijing Chao Yang Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06733311
- Sponsor's protocol ID
- CYFH202324
- Other IDs
- CYFH202324
- Acronym
- UPD-LC
- Status last verified
- Feb 2024
- First submitted
- 10 Dec 2024
- Last updated
- 13 Dec 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Gasparri R, Sedda G, Caminiti V, Maisonneuve P, Prisciandaro E, Spaggiari L. Urinary Biomarkers for Early Diagnosis of Lung Cancer. J Clin Med. 2021 Apr 16;10(8):1723. doi: 10.3390/jcm10081723.
PubMed 33923502
Study at a glance
From the registry record, last updated by the sponsor on 13 Dec 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Urine Proteomic Precision Diagnosis Model for Early Stage Lung Cancer
- ClinicalTrials.gov ID
- NCT06733311
- Acronym / sponsor ID
- UPD-LC / CYFH202324
- Conditions
- Early-Stage Lung Cancer, NSCLC, Pulmonary Nodule
- Keywords
- Early-Stage Lung Cancer, Pulmonary Nodule, Urine Proteomics, Non-Invasive Diagnosis, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 480 participants (estimated)
- Lead sponsor
- Beijing Chao Yang HospitalUniversity, hospital or other
- Study start
- 1 Mar 2024 (actual)
- Primary completion
- 31 Dec 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 13 Dec 2024 (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.
Brief Summary:
The goal of this observational study is to develop a non-invasive urine proteomic diagnostic model to improve early-stage lung cancer detection. The study aims to answer the following main questions:
Can urine proteomics reliably differentiate early-stage lung cancer from benign conditions? How does the diagnostic model compare to current clinical and imaging methods in accuracy?
Participants will:
Provide preoperative urine samples. Undergo proteomic analysis of urine samples. Have clinical, imaging, and proteomic data integrated into an AI-assisted diagnostic model.
The study will evaluate the sensitivity and specificity of this innovative diagnostic approach.
Detailed description
Detailed Description:
This study focuses on developing a urine proteomic-based diagnostic model to improve the early detection of lung cancer. It leverages non-invasive urine sampling, proteomic analysis, and artificial intelligence to create a high-sensitivity, high-specificity diagnostic tool.
The study will recruit 480 participants with suspected early-stage lung cancer (I-IIIA, non-N2). Urine samples will be collected before surgery, and participants will undergo standard imaging and diagnostic evaluations, including chest CT, abdominal ultrasound or CT, brain MRI or CT, and bone scans.
The primary objectives of the study include:
- Biomarker Identification: Identifying differentially expressed urine proteins associated with early-stage lung cancer.
- Diagnostic Model Construction: Combining proteomic findings with clinical and imaging data to construct a diagnostic model using AI-based algorithms.
- Validation: Evaluating the model's diagnostic accuracy compared to current clinical practices.
Participants will contribute to the advancement of a novel diagnostic method that aims to minimize unnecessary invasive procedures and improve lung cancer prognosis through early and accurate detection.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 2 Years
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Urine Proteomics Diagnostic Group | – | –Participants in this group will undergo urine proteomic analysis before surgery to predict early-stage non-small cell lung cancer (NSCLC). The predictions include tumor histopathological subtypes, lymph node metastasis, and other pathological factors. The accuracy of the diagnostic model will be compared to pathological results after surgery. This group consists of approximately 240 participants, with an anticipated 10% loss accounted for. |
| CT Diagnostic Group | – | –Participants in this group will undergo standard preoperative chest CT imaging to predict early-stage non-small cell lung cancer (NSCLC). Predictions include tumor histopathological subtypes, lymph node metastasis, and other pathological factors. The accuracy of the imaging predictions will be compared to pathological results after surgery. This group also consists of approximately 240 participants, with an anticipated 10% loss accounted for. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population includes patients suspected of having early-stage (I-IIIA, non-N2) non-small cell lung cancer (NSCLC), recruited from the thoracic surgery and respiratory departments of Beijing Chao-Yang Hospital and collaborating clinical centers. Participants are individuals scheduled for surgical intervention based on preoperative clinical and imaging assessments.
Key inclusion criteria (5 listed)
- Male or female participants aged 18 to 75 years.
- Diagnosed or highly suspected early-stage (I-IIIA, non-N2) non-small cell lung 3.cancer (NSCLC) based on imaging or clinical assessment.
- 4.No prior anti-cancer treatment, including surgery, chemotherapy, radiotherapy, targeted therapy, or immunotherapy.
- 5.Able to provide informed consent and willing to comply with the study protocol, including urine sample collection before surgery.
- 6.Diagnosis confirmed within 42 days post-imaging or preoperative assessment through biopsy or surgical specimen.
Key exclusion criteria (6 listed)
- History of any cancer treatment prior to study enrollment.
- Presence of metastatic disease (N2 or more advanced staging).
- Severe comorbid conditions or organ dysfunctions (e.g., renal failure) that could affect urine sample quality or interpretation.
- Pregnancy or lactation.
- Participation in another clinical study that could interfere with the outcomes of this study.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Prediction Accuracy of Diagnostic Models
Time frame: Within 2 weeks post-surgery.
The primary outcome measure is the accuracy of preoperative predictions (sensitivity and specificity) for early-stage non-small cell lung cancer (NSCLC) diagnosis. Predictions are based on: 1. Urine proteomics in the experimental group. 2. Chest CT imaging in the control group. Accuracy will be assessed by comparing preoperative predictions with postoperative pathological findings, including tumor histopathological subtypes, lymph node metastasis, and other pathological factors.
3 secondary outcomes
Cut-off Value for Urine Proteomics Diagnostic Test
Time frame: Within 1 month after data analysis.
Comparative Performance of Diagnostic Models
Time frame: Within 2 months post-surgery.
Long-term Diagnostic Effectiveness
Time frame: Up to 2 years post-surgery.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Dec 2024.
- Background: PubMed 33923502Gasparri R, Sedda G, Caminiti V, Maisonneuve P, Prisciandaro E, Spaggiari L. Urinary Biomarkers for Early Diagnosis of Lung Cancer. J Clin Med. 2021 Apr 16;10(8):1723. doi: 10.3390/jcm10081723.
- 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. This study is not recruiting at the moment.
Bin Hu, MD contact
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 Early-Stage Lung Cancer and NSCLC, run by Beijing Chao Yang Hospital 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 5 inclusion and 6 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: status not confirmed, as of the sponsor's last update on 13 Dec 2024. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Bin Hu, MD (+86 139-0130-1750), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction Accuracy of Diagnostic Models (time frame: Within 2 weeks post-surgery.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06733311, which also lists 1 linked publication.
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 13 Dec 2024.