SERS-Based Serum Molecular Spectral Screening for Hematogenous Metastasis

SERS-Based Serum Molecular Spectral Screening for Hematogenous Metastasis vs. Non-Metastasis in Non-Small Cell Lung Cancer: A Multicenter, Open-Label, Double-Blind, Independent Data Analysis Clinical Trial

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Lung Cancers
• Intervention / Treatment: Diagnostic test: Serum Raman spectroscopy intelligent diagnostic system

Detailed Description

This study used a confocal Raman microspectrometer produced by Renishaw, Britain, purchased by the Key Laboratory of the School of Optoelectronics and Engineering of Fujian Normal University. The spectral resolution was 2 cm-1, the excitation wavelength was 785 nm, and a 20x objective Leica microscope was used to collect SERS spectra in the range of 400-1800 cm-1. The excitation irradiation time of each spectrum was 1 s, and the laser power was 30 mW. The measured SERS spectra were collected using the WIRE3.4 (Renishaw) software package. In order to reduce the interference of fluorescence background signals between different spectral lines, the Vancouver Raman Algorithm software (multi-order polynomial fitting algorithm) was used to remove the fluorescence background, remove the baseline and smooth the results. At the same time, in order to avoid changes in peak spectrum intensity caused by instrument performance problems, the spectrum after background subtraction was normalized using NILabVIEW2014 software. Then, the obtained spectral data was analyzed for mean spectrum and charts using Origin, and multivariate statistical analysis was performed.

• Study Type: Observational
• Enrollment (Estimated): 200

Contacts and Locations

• Study Contact: Name: Zongyang Yu, Ph.D; Phone Number: 22859650 13509327806; Email: yuzy527@sina.com

Study Locations

• China
  • Fujian
    • Fuzhou, Fujian, China
      • Raman detector
      • Contact: Zongyang Yu, Ph.D; Email: yuzy527@sina.com

Participation Criteria

Eligibility Criteria

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

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

1. Participants with concomitant other malignant tumors;
2. Participants with missing baseline clinical data;
3. 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;
4. Participants who do not cooperate or refuse to participate in clinical trials at a later stage.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Time to RAMAN diagnosis	The time to perform RAMAN testing and obtain diagnostic results after obtaining serum	up to 30 days
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

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Safety assessment Results	AEs and SAEs through Day 30	up to 30 days

Collaborators and Investigators

• Sponsor: Fuzhou General Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): April 9, 2026
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): June 1, 2026

Study Registration Dates

• First Submitted: January 4, 2025
• First Submitted That Met QC Criteria: January 8, 2025
• First Posted (Actual): March 25, 2025

Study Record Updates

• Last Update Posted (Actual): March 31, 2025
• Last Update Submitted That Met QC Criteria: March 26, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: NSCLC; Raman; SERS; diagnostic model
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms
• Other Study ID Numbers: 2024-044

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No