SERS-Based Serum Molecular Spectral Screening for Benign and Malignant Pulmonary Proliferative Nodules

NCT06775587 | Not Yet Recruiting DMC

• 1 other identifier: 2024-041
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

Pulmonary nodules are often an early indicator of lung cancer. With the widespread adoption of chest CT scans in routine physical examinations, an increasing number of pulmonary nodules are being detected, including a variety of small nodules such as inflammatory lesions, benign tumors, and malignant tumors. Currently, there is no unified international consensus on the diagnostic and treatment strategies for pulmonary nodules, as outlined by various global guidelines. Developing and implementing a comprehensive lung nodule and lung cancer screening program within public health management systems remains a complex and challenging endeavor. Advancing research and proposing lung cancer screening technologies that are highly sensitive, highly specific, simple, accessible, and cost-effective is an essential and pressing priority in modern healthcare. Raman spectroscopy (RS), as a non-invasive and highly specific molecular detection technique, can be obtained at the molecular level to sensitively detect changes in biomolecules composed of proteins, nucleic acids, lipids, and sugars related to tumor metabolism in biological samples. The surface enhanced Raman spectroscopy (SERS) developed based on this technology is one of the feasible methods for high-sensitivity biomolecule analysis. Although SERS technology has shown good diagnostic efficacy in lots of preclinical studies in multiple tumors, it is limited to a generally small sample size and lacks external validation. There for, a clinical study of Raman spectra for tumor diagnosis is needed, which meets the following requirements: 1.An objective, fast and practical application of Raman spectral data processing is needed and deep learning method may be the best classification method; 2. It requires multicenter and large clinical samples to train deep learning diagnostic model, and verify its true efficacy through external data of prospective study. In preliminary research, the investigators collected serum Raman spectroscopy data from a cohort of 191 patients with pulmonary nodules and developed an intelligent diagnosis system for distinguishing between benign and malignant pulmonary nodules using a machine learning model. The system achieved an accuracy of 89.7%. In order to obtain the highest level of clinical evidence and truly realize clinical transformation, this prospective, multi-center clinical study is designed to verify the intelligent diagnostic system for early diagnosis of prostate cancer.

Conditions

Lung Cancer in Normal and Malignant Tumors

Keywords

SERS; Raman; Early screening for lung cancer; Pulmonary nodule; diagnostic model

Outcome Measures

Primary Outcomes (2)

• Postoperative pathological results

  After undergoing surgical resection of pulmonary nodules, the final pathological nature of the pulmonary nodules was determined through pathological examination.

  through study completion, an average of 1 year

• 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 Outcomes (2)

• Time to RAMAN diagnosis

  up to 30 days

• Safety assessment Results

  up to 30 days

Study Arms (1)

Chest CT confirms patient with pulmonary nodules

Chest CT confirmed the presence of pulmonary nodules in the patient and ultimately underwent surgical intervention. The pulmonary nodules had the final pathological results.

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.

Chest CT confirms patient with pulmonary 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

Chest CT reveals the presence of pulmonary nodules in the patient and plans to undergo surgical treatment

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, 350000, China

Location

MeSH Terms

Conditions

Neoplasms

Central Study Contacts

Zongyang Yu, Ph.D

CONTACT

13509327806; yuzy527@sina.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Year
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 24, 2024
• First Posted: January 15, 2025
• Study Start: April 8, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: March 31, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP
• Access Criteria: For any reasonable needs related to scientific research, please contact the project leader for specific data consultation.

Locations

CN (1)