Lung Cancer Clinical Trial
Official title:
Biomarker Discovery for Toxicity and Survival in Radiation Oncology: An Integrative Molecular-Clinical Approach
Verified date | November 2023 |
Source | Ohio State University Comprehensive Cancer Center |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
This research trial studies metabolomic and other molecular profiling to identify predictive biomarkers for radiation toxicity and survival in patients with lung or brain cancers receiving radiation therapy. Studying samples of blood, urine, and tissue from patients with lung or brain cancer in the laboratory may help doctors identify and learn more about biomarkers related to cancer and predict which patients are at higher risk for developing radiation side effects and how well patients will respond to radiation treatment.
Status | Active, not recruiting |
Enrollment | 273 |
Est. completion date | December 30, 2025 |
Est. primary completion date | December 30, 2024 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - Consultation with the Radiation Oncology department of the Ohio State University (OSU) Comprehensive Cancer Center - The following diagnoses will be included: malignant neoplasm of the brain; primary malignant neoplasm of trachea bronchus and lung; primary malignant neoplasm of the cervix uteri and primary malignant neoplasm the prostate Exclusion Criteria: - Being an inmate |
Country | Name | City | State |
---|---|---|---|
United States | Arthur G. James Cancer Hospital and Solove Research Institute at Ohio State University Medical Center | Columbus | Ohio |
Lead Sponsor | Collaborator |
---|---|
Ohio State University Comprehensive Cancer Center |
United States,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Potential predictive biomarkers for the development of radiation pneumonitis for lung cancer patients | For each biomarker, linear mixed model (with group and time interaction and repeated measurement for each patient over time) on the log-transformed expression level will be used to detect if it is differentially expressed after controlling for the effects of the co-variates (such as age, disease status, and treatment details). | Baseline | |
Primary | Potential predictive biomarkers for the development of radiation necrosis for brain cancer patients | For each biomarker, linear mixed model (with group and time interaction and repeated measurement for each patient over time) on the log-transformed expression level will be used to detect if it is differentially expressed after controlling for the effects of the co-variates (such as age, disease status, and treatment details). | Baseline | |
Secondary | Biomarkers that are prognostically significant on disease free or overall survival, identified using the survival analysis | Kaplan Meier analysis and logrank test to identify biomarkers that are prognostically significant. | Baseline |
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