SERS-Based Serum Molecular Spectral Screening for Hematogenous Metastasis

NCT06772363 | Not Yet Recruiting DMC

• 1 other identifier: 2024-044
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

Although modern medicine has made significant progress in the diagnosis and treatment of lung cancer, most patients are diagnosed at locally advanced stage or with distant metastases, especially in the late stages where the cancer has spread to other organs through hematogenous metastasis. This not only significantly the survival rate of patients but also increases the complexity and difficulty of treatment. Hematogenous metastasis plays an important role in the clinical progression of lung cancer, its complex biological processes pose a huge challenge for clinical management. Early detection of hematogenous metastasis is difficult, and traditional imaging methods have limited sensitivity in detecting small metastatic lesions. The emerging technology of circulating tumor cells (CTCs) has been limited in clinical application due to its high detection costs and technical requirements. Therefore researching and developing high-sensitivity, high-specificity, simple, easy-to-popularize, and low-cost technologies to predict the risk of hematogenous metastasis lung cancer is crucial for early diagnosis and more precise treatment. Raman spectroscopy (RS), a non-invasive and highly specific molecular detection technology, can detect in biomolecules such as proteins, nucleic acids, lipids, and sugars related to tumor metabolism in biological samples at the molecular level. Surface-enhanced R spectroscopy (SERS), developed based on this technology, is one of the feasible methods for high-sensitivity biomolecular analysis. Although SERS technology has shown diagnostic results in numerous preclinical studies of various tumors, it is limited by small sample sizes and lacks external validation. Therefore, clinical studies on the diagnosis of tumors Raman spectroscopy are needed, with the following requirements: 1. Objective, rapid, and practical Raman spectroscopy data processing methods are needed, and and deep learning methods may be the best classification methods; 2. Multicenter, large-sample clinical samples are needed to train deep learning diagnostic models, and real-world performance should be validated through external data from prospective studies. In previous study, the investigators collected serum Raman spectroscopy data from a cohort of 23 patients with lung malignancies and developed an intelligent Raman diagnostic system for hematogenous metastasis in non-small cell lung cancer (NSCLC) based on learning models, with an accuracy rate of 95%. To obtain the highest level of clinical evidence and truly achieve clinical translation, this prospective, multicenter clinical aims to validate the use of this intelligent diagnostic system for early diagnosis of hematogenous metastasis in NSCLC.

Conditions

Lung Cancers

Keywords

SERS; NSCLC; Raman; diagnostic model

Outcome Measures

Primary Outcomes (2)

• Diagnostic accuracy

  Determine whether there is hematogenous metastasis in enrolled lung cancer patients through RAMAN intelligent diagnostic system

  through study completion, an average of 1 year

• Time to RAMAN diagnosis

  The time to perform RAMAN testing and obtain diagnostic results after obtaining serum

  up to 30 days

Secondary Outcomes (1)

• Safety assessment Results

  up to 30 days

Study Arms (1)

Patients who underwent chest CT scans and were found to have lung nodules

Patients who underwent chest CT scans and were found to have lung nodules and underwent surgical resection

Diagnostic Test: Serum Raman spectroscopy intelligent diagnostic system

Interventions

Serum Raman spectroscopy intelligent diagnostic system DIAGNOSTIC_TEST

1\. Screening interested participants should sign the appropriate informed consent (ICF) prior to completion any study procedures. 2. The investigator will review symptoms, risk factors, and other non-invasive inclusion and exclusion criteria. 3. The following is the general sequence of events during the 3 months evaluation period: 4. Completion of baseline procedures Participants were assessed for 3 months and completed all safety monitoring.

Patients who underwent chest CT scans and were found to have lung nodules

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The participants were diagnosed with lung malignancy through pathological examination and were able to undergo clinical staging based on TNM.

You may qualify if:

• Participants with Lung cancer meeting the criteria of TNM (Ninth Edition);
• Participants are willing to participate in this study and follow the research plan;
• Participants or legally authorized representatives can give written informed consent approved by the Ethics Review Committee that manages the website;

You may not qualify if:

• Participants with concomitant other malignant tumors;
• Participants with missing baseline clinical data;
• Participants with severe underlying lung diseases (such as bronchiectasis, bronchial asthma or COPD, etc.), or those with a history of occupational or environmental exposure to dust, mines or asbestos;
• Participants who do not cooperate or refuse to participate in clinical trials at a later stage.

Sponsors & Collaborators

• Fuzhou General Hospital: lead

Study Sites (1)

Raman detector

Fuzhou, Fujian, China

Location

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Central Study Contacts

Zongyang Yu, Ph.D

CONTACT

13509327806; yuzy527@sina.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 4, 2025
• First Posted: January 13, 2025
• Study Start: April 9, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026
• Last Updated: March 31, 2025

Record last verified: 2025-03

Locations

CN (1)