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

Capital Medical University

Status: Enrolling

Conditions: Pulmonary Nodule; Early-Stage Lung Cancer; NSCLC

Study type: Observational

Funder types: Other

Identifiers

NCT06733311

CYFH202324 (Other Grant/Funding Number)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 480 estimated patients

Sex: All

Ages: 18 to 75 years old

Volunteers: No Healthy Volunteers

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.

Trial design

480 participants in 2 patient groups

Urine Proteomics Diagnostic Group

Description:

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

Description:

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.