Clinical trial NCT05448703: A Study on Predictive Models and Clinical Outcome of Radiation Pneumonitis
Official title: A Multicenter Prospective Observational Study on Predictive Models and Clinical Outcome of Radiation Pneumonitis
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Radiation Pneumonitis
Lead sponsor: Huazhong University of Science and Technology
Sponsor class: OTHER
Collaborators: name: Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology
name: Jingjiang People' Hospital
First posted: 2022-07-07
Enrollment: 300
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Lung cancer patients treated with thoracic radiotherapy.

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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Radiation pneumonitis
timeFrame: 12 months after radiotherapy
measure: Overall Survival
timeFrame: 5 years after radiotherapy
Secondary outcomes:
measure: Other injuries induced by radiotherapy
timeFrame: 5 years after radiotherapy
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology
state: Hubei
city: Wuhan
country: China
facility: Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
state: Hubei
city: Jingjiang
country: China
facility: Jingjiang People' Hospital
state: Jiangsu
Study officials:
affiliation: Tongji Hospital
name: Xianglin Yuan, PhD
role: PRINCIPAL_INVESTIGATOR
References: