Multi-omics Analysis to Characterize the Invasive Evolution of Pulmonary Subsolid Nodules

Not yet recruiting

• Conditions: Adenocarcinoma of Lung
• Interventions: Diagnostic Test: Mass spectrometry-based proteomics

• Registration Number: NCT06215885
• Lead Sponsor: Cancer Institute and Hospital, Chinese Academy of Medical Sciences

Brief Summary

Current clinical follow-up frequency and treatment timing for pulmonary subsolid nodules (SSNs) rely mostly on whether the nodules grow, which may not accurately reflect the pathological status, and may lead to unnecessary follow-ups. This study aims to use multi-omics techniques to dynamically observe the growth and invasiveness evolution process of SSNs and uncover its invasiveness mechanism. Radiological characteristics of SSNs in different invasiveness stages were also analyzed and summarized by analyzing preoperative CT. This can overcome the bottleneck of invasiveness assessment in the growth process of SSN and provide scientific evidence for the scientific management and clinical treatment timing choice of SSN patients, thus facilitating the rational allocation of medical resources and prolonging the expected survival of national health.

Detailed Description

This prospective observational cohort study aims to recruit 120 patients with subsolid nodules (SSNs) and 100 healthy volunteers. Enroll 120 patients with SSNs planned for surgery and 100 healthy volunteers. Sequence blood and tissue samples from patients and compare the relevance of biomarkers between the two. Use blood from healthy volunteers as blank controls. Additionally, analyzes radiological characteristics of SSNs at different invasive stages using preoperative CT.

Study Timeline

• Status Verified: 2023-10
• Study Start: 2024-01
• Study First Submitted: 2024-01-01
• Study First Submitted QC: 2024-01-18
• Last Update Submitted: 2024-01-18
• Study First Posted: 2024-01-22
• Last Update Posted: 2024-01-22
• Primary Completion: 2024-12
• Study Completion: 2024-12

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 220

Inclusion Criteria

Patients with SSNs

1. Newly diagnosed patients with persistent SSNs confirmed by thin-section chest CT examination
2. Patient areilling to undergo surgery
3. Voluntarily sign a written informed consent form

Healthy Volunteers

1. Healthy volunteers aged 18-50 years, regardless of gender.
2. No lung nodules detected on chest thin-section CT.
3. No history of cancer.
4. Voluntary sign a written informed consent form.

Exclusion Criteria

Patients with SSNs

1. Surgical contraindications
2. Inability to cooperate with CT/MR examination to obtain high-quality images
3. History of malignant tumors
4. Previous targeted, immune, or ablation therapy
5. Postoperative pathology of non-lung adenocarcinoma disease spectrum
6. Other situations deemed unsuitable for participation in this study by the researchers

Healthy Volunteers

1. Drug abuse
2. HIV infection or AIDS
3. History of syphilis, gonorrhea, or other infectious diseases
4. Hepatitis B or C virus carrier

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Subsolid nodules group	Mass spectrometry-based proteomics	Patients with pulmonary subsolid nodules were identified by CT examination and intended for surgical treatment. All participants will undergo chest CT, genomic, and proteomic tests to identify related biomarkers and establish a clinical diagnostic model for the invasiveness of subsolid nodules.
Volunteers group	Mass spectrometry-based proteomics	Healthy volunteers.

Primary Outcome Measures

Name	Time	Method
Accuracy of subsolid nodule invasive diagnosis	Through study completion, an average of 1 year.	The efficacy of screening features for SSN invasiveness was compared to pathological diagnosis, using metrics such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Trial Locations

Locations (1)

Cancer Hospital Chinese Academy of Medical Sciences; 🇨🇳 Beijing, Beijing, China