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

NCT06775587 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2025-03-31

No results posted yet for this study

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

Interventions

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.

Sponsors & Collaborators

  • Fuzhou General Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-04-08
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06775587 on ClinicalTrials.gov