A Study on Predictive Models and Clinical Outcome of Radiation Pneumonitis

Huazhong University of Science and Technology

Status: Enrolling

Conditions: Radiation Pneumonitis

Treatments: Other: Blood Samples for Biomarkers

Study type: Observational

Funder types: Other

Identifiers

NCT05448703

TJCC012

Details and patient eligibility

About

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.

Full 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.

Enrollment: 300 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. Clinical diagnosis of lung cancer by histology
2. Radiation dose at least 45 Gy
3. Karnofsky >60
4. Age>18
5. Life expectancy of at least 6 months

Exclusion criteria

1. Previous thoracic irradiation
2. Severe cardiopulmonary diseases

Trial design

300 participants in 1 patient group

Group 1

Description:

Lung cancer patients treated with thoracic radiotherapy

Treatment:

Other: Blood Samples for Biomarkers