Actively Recruiting
Study of Online Adaptive Stereotactic Radiotherapy Without PTV Margins for Early-stage Non-small Cell Lung Cancer or Lung Oligometastases
Led by Sun Yat-sen University · Updated on 2025-11-17
130
Participants Needed
1
Research Sites
52 weeks
Total Duration
AI-Summary
What this Trial Is About
Researchers are evaluating the safety and effectiveness of removing the planning target volume PTV margins during online adaptive stereotactic body radiotherapy SBRT for patients with early-stage non-small cell lung cancer NSCLC or pulmonary oligometastases. The study focuses on how this approach compares to standard SBRT in treating these lung conditions, aiming to improve treatment precision and patient outcomes in a controlled clinical setting. Participants are randomly assigned to one of two groups. The study group receives online adaptive SBRT without the usual PTV expansion margin, with a total radiation dose of 45-50 Gy delivered in 3 to 4 daily sessions. Both FBCT and 4DCT scans are used before each treatment session. The control group receives conventional SBRT with PTV expansion at the same dose and schedule, using CBCT scans prior to each session. This randomized design helps compare the new method against the standard approach. During the study, patients will be closely monitored through regular follow-up visits to evaluate safety and treatment effectiveness. Researchers will assess respiratory toxicity rates one year after treatment as the primary outcome. Secondary outcomes include tumor response two months post-radiotherapy and local tumor control over two years. Various tests and imaging techniques will be used to track progress and any side effects. The study duration includes treatment and extended observation to ensure comprehensive safety and efficacy data collection.
CONDITIONS
Brief Title
Elimination of PTV Margins Based on Online Adaptive Stereotactic Radiotherapy for Early-stage Non-small Cell Lung Cancer or Pulmonary Oligometastases
Research Team
B
Bo Qiu, Professor
H
Hui Liu, Professor
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