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

Assessment the Association of Imaging and Tumor Microenvironment in Lung Cancer With Radiogenomics

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06500312

Enrollment

150

Registered

2024-07-15

Start date

2024-07-02

Completion date

2027-07-02

Last updated

2025-01-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lung Cancer

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

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。

Design outcomes

Primary

Measure	Time frame	Description
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

Measure	Time frame	Description
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.

Countries

China

Contacts

Primary ContactLian Yang

yanglian@hust.edu.cn18986273791

Outcome results