Multi-Omics Prediction of Radiation Lung Injury in Immunotherapy-Treated Lung Cancer Patients
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorHAN GUANG
About this trial
This prospective study aims to develop a multi-omics-based predictive model for radiation pneumonitis in lung cancer patients receiving sequential immunotherapy and thoracic radiotherapy.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
None
Disqualifiers
None
Trial population
This prospective observational cohort study will enroll adult patients with histologically confirmed lung cancer who have received prior immunotherapy and are scheduled to undergo thoracic radiotherapy. Participants will be recruited from the Department of Oncology at Hubei Cancer Hospital. Eligible patients must have no use of antibiotics within 1 week prior to radiotherapy initiation, with no active infection or uncontrolled comorbidities. The study aims to collect blood, urine, and stool samples at baseline for multi-omics profiling. A total of approximately 160 participants will be enrolled.
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
No Intervention: Observational Cohort
Other interventionThis study involves collection of peripheral blood, urine, and stool samples from lung cancer patients who have received immunotherapy followed by thoracic radiotherapy. Samples are obtained at baseline prior to the initiation of radiotherapy for comprehensive multi-omics profiling, including genomic, transcriptomic, proteomic, and metabolomic analyses. No therapeutic intervention is administered as part of this study.
Treatment groups
160 Participants
are divided into 1 treatment groupGroup A: Lung Cancer Patients Receiving Sequential Immunotherapy and Thoracic Radiotherapy1 intervention
Trial outcomes
Primary outcomes
1
Area Under the Receiver Operating Characteristic Curve (AUC) of the Multi-Omics Prediction Model for Radiation Pneumonitis
The area under the ROC curve (AUC) will be calculated to evaluate the predictive performance of a multi-omics-based model-integrating genomic, transcriptomic, proteomic, and metabolomic data-for distinguishing lung cancer patients who develop grade ≥2 radiation pneumonitis from those who do not, following sequential immunotherapy and thoracic radiotherapy. Model performance will be assessed using bootstrap internal validation with 1,000 resamples to estimate optimism-corrected AUC.
Time frame
At 6 months post-radiotherapySponsors and contacts
Click on the lead sponsor to view all of their trials.
HAN GUANG
Lead sponsor
Hubei Cancer Hospital
Sponsor institution
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Collaborator
Source ClinicalTrials.gov
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