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

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.

Study Overview

• Status: Recruiting
• Conditions: NSCLC; Pulmonary Nodule; Early-Stage Lung Cancer

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.

• Study Type: Observational
• Enrollment (Estimated): 480

Contacts and Locations

• Study Contact: Name: Bin Hu, MD; Phone Number: +86 139-0130-1750

Study Locations

• China
  • Beijing
    • Chaoyang District, Beijing, China, 100000
      • Recruiting
      • Beijing Chao-Yang Hospital, Capital Medical University
      • Contact: Bin Hu, MD; Phone Number: +86 139-0130-1750; Email: cyyyhubin@163.com
      • Contact: Fanjie Meng, MD; Phone Number: +86 188-2266-5731; Email: mfjwill99@gmail.com
      • Principal Investigator: Xincheng Li, Master of Medicine in progress

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
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 the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Within 2 weeks post-surgery.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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).	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.	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).	Up to 2 years post-surgery.

Collaborators and Investigators

• Sponsor: Beijing Chao Yang Hospital

Publications and helpful links

General 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.

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2024
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: December 10, 2024
• First Submitted That Met QC Criteria: December 10, 2024
• First Posted (Estimated): December 13, 2024

Study Record Updates

• Last Update Posted (Estimated): December 13, 2024
• Last Update Submitted That Met QC Criteria: December 10, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Pulmonary Nodule; Early-Stage Lung Cancer; Urine Proteomics; Non-Invasive Diagnosis
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Multiple Pulmonary Nodules
• Other Study ID Numbers: CYFH202324 (Other Grant/Funding Number: Beijing Institute of Respiratory Medicine)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Study Data/Documents

1. Informed Consent Form
   Information identifier: ICF-UPDLC-2023
   Information comments: This identifier is specific to the informed consent form for the study titled "Urine Proteomic Diagnostic Model for Early-Stage Lung Cancer". It can be used to search the designated data repository or request the document from the study administrators.
2. Study Protocol
   Information identifier: SP-UPDLC-2023
   Information comments: This link provides access to the official project task document for the study titled "Urine Proteomic Diagnostic Model for Early-Stage Lung Cancer". The document includes detailed descriptions of the research objectives, tasks, timelines, and expected outcomes, serving as a comprehensive guide for the study implementation.
3. Ethics Approval Document
   Information identifier: EA-UPDLC-2023
   Information comments: This document provides the ethics approval for the study "Urine Proteomic Diagnostic Model for Early-Stage Lung Cancer". It confirms compliance with ethical standards for the protection of participant rights and safety throughout the research process.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No