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Retrospective and Prospective Application of a Machine Learning Model Integrating Multidimensional Data to Predict Radiation Pneumonitis

Retrospective and Prospective Application of a Machine Learning Model Integrating Multidimensional Data to Predict Radiation Pneumonitis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500102055
Enrollment
Unknown
Registered
2025-05-08
Start date
2025-05-12
Completion date
Unknown
Last updated
2025-05-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

radiation pneumonitis

Interventions

Retrospective cohort:None
Prospective cohort:None

Sponsors

The Second Affiliated Hospital of Chongqing Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 85 Years

Inclusion criteria

Inclusion criteria: Inclusion Criteria 1. Age >= 18 years, histologically confirmed primary lung cancer (including adenocarcinoma, squamous cell carcinoma, small-cell lung cancer, or other subtypes). 2. No prior thoracic surgery or other treatments affecting the chest region. 3. Eastern Cooperative Oncology Group (ECOG) performance status = 3 months. 4. Availability of chest CT scans at approximately 1 month, 3 months, and 6 months (+- 15 days) after completion of radiotherapy for imaging-based assessment of lung changes. 5. Fully informed about the study, willing to participate, able to comply with treatment and follow-up procedures, and has provided written informed consent.

Exclusion criteria

Exclusion criteria: Exclusion Criteria 1. Interruption of radiotherapy for more than one week or inability to complete the prescribed radiotherapy course. 2. Incomplete imaging data or inability to perform follow-up assessments. 3. Pre-existing severe pulmonary disease (e.g., severe COPD, interstitial lung disease) that would confound risk assessment for radiation pneumonitis. 4. Participant withdrawal of informed consent. 5. Other circumstances deemed by the investigator to warrant discontinuation from the study.

Design outcomes

Primary

MeasureTime frame
Incidence rate of radiation pneumonitis;

Secondary

MeasureTime frame
Independent contributions of radiomics, digital pathology, and dosimetric features to the prediction of radiation pneumonitis.;OS,PFS,DFS;

Countries

China

Contacts

Public ContactZhengzhou Yang

The Second Affiliated Hospital of Chongqing Medical University

2023440076@stu.cqmu.edu.cn+86 138 8327 0881

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Apr 30, 2026