Decoding the Association of Imaging and Tumor Microenvironment in Lung Cancer Using Radiogenomic Approach（Radiogenomics-Lung）

Recruiting

• Conditions: Lung Cancer

• Registration Number: NCT06500312
• Lead Sponsor: Wuhan Union Hospital, China

Brief Summary

This is the prospective, observational cohort study (Radiogenomics-Lung), which aims to explore the relationship between the imaging information of lung cancer patients, tumor microenvironment and the prognosis of lung cancer patients.

Detailed Description

Not available

Study Timeline

• Status Verified: 2024-07
• Study Start: 2024-07-02
• Study First Submitted: 2024-07-08
• Study First Submitted QC: 2024-07-08
• Study First Posted: 2024-07-15
• Last Update Submitted: 2025-01-09
• Last Update Posted: 2025-01-13
• Primary Completion: 2027-07-02
• Study Completion: 2027-07-02

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 150

Inclusion Criteria

1. Patients were treated for lung cancer in Wuhan Union Hospital from July 2024 to July 2027;
2. Aged > 18 years old;
3. At least one chest CT scan before treatment;
4. Tissue biopsy pathological examination confirmed the diagnosis of the above tumors.

Exclusion Criteria

1. Poor image quality;
2. Incomplete clinical data or loss of follow-up;
3. Presence of another primary malignancy other than lung cancer;
4. Unclear pathological diagnosis。

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Correlation analysis of sequencing results and body composition	0.5 year	Conducting correlation analysis between body composition and results of multi-omics sequencing
Correlation analysis of sequencing results and tumor radiomic features	0.5 year	Conducting correlation analysis between radiomic features and results of multi-omics sequencing

Secondary Outcome Measures

Name	Time	Method
Constructing tumor microenvironment prediction model	2 years	Constructing radiomic model to predict tumor microenvironment. The indicator is the area under the curve (AUC) of the prediction model
Constructing a multi-dimensional prognostic prediction model	3 years	A high-dimensional model was constructed to predict the prognosis of patients through multi-omics sequencing data, body composition, and tumor radiomic features. The indicator is the area under the curve (AUC) of the prediction model.

Trial Locations

Locations (1)

Union Hospital，Tongji Medical College，Huazhong University of Science and Technology; 🇨🇳 Wuhan, Hubei, China