A Study Developing a Non-invasive Urine-based Proteomic Model for Early Lung Cancer Detection.

NCT ID: NCT06733311

Last Updated: 2024-12-13

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 480 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-03-01
• Study Completion Date: 2024-12-31

Brief 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 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:

1. Biomarker Identification: Identifying differentially expressed urine proteins associated with early-stage lung cancer.

2. Diagnostic Model Construction: Combining proteomic findings with clinical and imaging data to construct a diagnostic model using AI-based algorithms.

3. 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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

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.

No interventions assigned to this group

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.

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

1. Male or female participants aged 18 to 75 years.

2. 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.

Exclusion Criteria

1. History of any cancer treatment prior to study enrollment.

2. Presence of metastatic disease (N2 or more advanced staging).

3. Severe comorbid conditions or organ dysfunctions (e.g., renal failure) that could affect urine sample quality or interpretation.

4. Pregnancy or lactation.

5. Participation in another clinical study that could interfere with the outcomes of this study.

6. Inability to comply with the study protocol, including language barriers or cognitive impairments.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Beijing Chao Yang Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Beijing Chao-Yang Hospital, Capital Medical University

Chaoyang District, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Bin Hu, MD

Role: CONTACT

Phone: +86 139-0130-1750

Facility Contacts

Bin Hu, MD

Role: primary

Fanjie Meng, MD

Role: backup

References

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.

Reference Type: BACKGROUND

PMID: 33923502 (View on PubMed)

Study Documents

Document Type: Informed Consent Form

View Document

Document Type: Study Protocol

View Document

Document Type: Ethics Approval Document

View Document

Other Identifiers

CYFH202324

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

CYFH202324

Identifier Type: -

Identifier Source: org_study_id