Ignite Creation Date:
2025-12-24 @ 7:24 PM
Ignite Modification Date:
2025-12-26 @ 3:50 PM
Study NCT ID:
NCT05448703
Status:
RECRUITING
Last Update Posted:
2022-10-06
First Post:
2022-07-01
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
A Study on Predictive Models and Clinical Outcome of Radiation Pneumonitis
Sponsor:
Huazhong University of Science and Technology
Organization:
Study Overview
Official Title:
A Multicenter Prospective Observational Study on Predictive Models and Clinical Outcome of Radiation Pneumonitis
Status:
RECRUITING
Status Verified Date:
2022-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Radiation pneumonitis is the main dose-limiting toxicity of thoracic radiotherapy, which can affect life quality, survival, and the tumor-controlling effects of patients receiving thoracic radiotherapy.
The purpose of this study is to:
* Identify biomarkers including serum proteins, gene expression, genetic changes, and epigenetic modifications that determine radiation pneumonitis.
* Investigate the relationship between radiation pneumonitis and other toxicities induced by radiotherapy.
* Construct a predictive model for radiation pneumonitis.
* Evaluate survival and treatment outcome of patients with radiation pneumonitis.
The purpose of this study is to:
* Identify biomarkers including serum proteins, gene expression, genetic changes, and epigenetic modifications that determine radiation pneumonitis.
* Investigate the relationship between radiation pneumonitis and other toxicities induced by radiotherapy.
* Construct a predictive model for radiation pneumonitis.
* Evaluate survival and treatment outcome of patients with radiation pneumonitis.
Detailed Description:
1. Collect clinical information, CT images, and peripheral blood of the lung cancer patients treated with thoracic radiotherapy in Tongji Hospital, Hubei Cancer Hospital, and Jingjiang People's Hospital.
2. Follow up the enrolled patients. All patients enrolled in this study are examined during and one month after radiotherapy. Then, the patients are followed every three months for the first year and every six months thereafter. At each follow-up visits, all patients are asked to undergo a chest CT, and information including survival status, symptoms, CT images, and treatment is collected. Radiation pneumonitis and other toxicities induced by radiotherapy are graded by two radiation oncologists according to the Common Terminology Criteria for Adverse Events 4.0 (CTCAE4.0).
3. Detect serum proteins, gene expression profile, single-nucleotide polymorphisms, and epigenetic modifications that may be associated with radiation pneumonitis.
4. Screen biomarkers that are associated with radiation pneumonitis via univariate and multivariate Cox regression analysis.
5. Construct a predictive model of radiation pneumonitis based on clinical information, radiomics, and biomarkers via machine learning or Least absolute shrinkage and selection operator.
6. Use Kaplan-Meier and Cox model to analyze the association of radiation pneumonitis with survival and efficacy of antitumor treatment.
7. Identify biomarkers and predictors of other toxicities induced by radiotherapy including radiation esophagitis, cardiotoxicity and radiodermatitis.
2. Follow up the enrolled patients. All patients enrolled in this study are examined during and one month after radiotherapy. Then, the patients are followed every three months for the first year and every six months thereafter. At each follow-up visits, all patients are asked to undergo a chest CT, and information including survival status, symptoms, CT images, and treatment is collected. Radiation pneumonitis and other toxicities induced by radiotherapy are graded by two radiation oncologists according to the Common Terminology Criteria for Adverse Events 4.0 (CTCAE4.0).
3. Detect serum proteins, gene expression profile, single-nucleotide polymorphisms, and epigenetic modifications that may be associated with radiation pneumonitis.
4. Screen biomarkers that are associated with radiation pneumonitis via univariate and multivariate Cox regression analysis.
5. Construct a predictive model of radiation pneumonitis based on clinical information, radiomics, and biomarkers via machine learning or Least absolute shrinkage and selection operator.
6. Use Kaplan-Meier and Cox model to analyze the association of radiation pneumonitis with survival and efficacy of antitumor treatment.
7. Identify biomarkers and predictors of other toxicities induced by radiotherapy including radiation esophagitis, cardiotoxicity and radiodermatitis.
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: