Clinical trial NCT06733311: A Study Developing a Non-invasive Urine-based Proteomic Model for Early Lung Cancer Detection.
Official title: Urine Proteomic Precision Diagnosis Model for Early Stage Lung Cancer
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Early-Stage Lung Cancer
NSCLC
Pulmonary Nodule
Keywords: Early-Stage Lung Cancer
Pulmonary Nodule
Urine Proteomics
Non-Invasive Diagnosis
Artificial Intelligence
Lead sponsor: Beijing Chao Yang Hospital
Sponsor class: OTHER
First posted: 2024-12-13
Enrollment: 480
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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.

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.

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.

Primary outcomes:
measure: Prediction Accuracy of Diagnostic Models
timeFrame: Within 2 weeks post-surgery.
Secondary outcomes:
measure: Cut-off Value for Urine Proteomics Diagnostic Test
timeFrame: Within 1 month after data analysis.
measure: Comparative Performance of Diagnostic Models
timeFrame: Within 2 months post-surgery.
measure: Long-term Diagnostic Effectiveness
timeFrame: Up to 2 years post-surgery.
Other outcomes:
Locations:
city: Chaoyang District
country: China
facility: Beijing Chao-Yang Hospital, Capital Medical University
state: Beijing
Study officials:
References:
citation: 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.