Clinical trial · Observational
A Study Developing a Non-invasive Urine-based Proteomic Model for Early Lung Cancer Detection.
Urine Proteomic Precision Diagnosis Model for Early Stage Lung Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Early-Stage Lung Cancer | Lung Neoplasm | PROBABILISTIC | 0.70 |
| NSCLC | Lung Non-Small Cell Carcinoma | ALIAS | 0.90 |
| Pulmonary Nodule | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- 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.
- label
- 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.
Primary outcomes (1)
- measure
- Prediction Accuracy of Diagnostic Models
- timeFrame
- Within 2 weeks post-surgery.
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 75 Years
Show eligibility criteria text
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.
References
Publications (1)
- BACKGROUNDGasparri 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. PMID 33923502