SERS-Based Serum Molecular Spectral Detection of Invasive Lung Cancer

900 Hospital of the People's Liberation Army Joint Logistic Support Force

Status: Begins enrollment in 1 month

Conditions: Lung Cancer Patients

Treatments: Diagnostic Test: Serum Raman spectroscopy intelligent diagnostic system

Study type: Observational

Funder types: Other

Identifiers

NCT06775015

2024-043

Details and patient eligibility

About

Surgery is the main treatment for early lung cancer. It is worth noting that there are significant differences in postoperative prognosis and surgical methods between microinvasive cancer and early-stage invasive cancer. Micro invasive lung cancer can achieve 100% long-term survival through surgical resection, without the need for postoperative adjuvant radiotherapy. There is no need to remove lung lobes during surgery, only segmental or wedge resection is required, and systematic lymph node dissection is not recommended. Therefore, accurate prediction of preoperative and intraoperative microinvasive cancer and invasive cancer in pulmonary nodules is crucial for patients to choose surgical methods, which can significantly affect postoperative lung function retention and overall survival.

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.

We collected serum Raman spectroscopy data from a cohort of 138 early lung cancer patients in our preliminary research. Based on a machine learning model, we constructed an early lung microinvasive cancer and invasive cancer Raman intelligent diagnosis system, which achieved an accuracy rate of 89.4%. To obtain the highest level of clinical evidence and truly achieve clinical translation, this prospective, multicenter clinical study aims to validate the use of this intelligent diagnostic system for early diagnosis of lung cancer and the discrimination between microinvasive cancer and invasive cancer.

Full description

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. Completion of baseline procedures, participants were assessed for 30 days and completed all safety monitoring.
4. After completing the baseline assessment and confirming enrollment, participants will be given 2ml of fasting venous blood.

Enrollment: 200 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Participants confirmed by chest CT to have pulmonary nodules
2. The diagnosis of participants with malignant pulmonary nodules must meet the TNM diagnostic criteria (Ninth Edition);
3. Participants are willing to participate in this study and follow the research plan;
4. 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.

Trial design

200 participants in 1 patient group

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

Description:

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

Treatment:

Diagnostic Test: Serum Raman spectroscopy intelligent diagnostic system