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Application of CT-Linac-Based "All-in-One" One-Stop Radiotherapy in Breast Cancer
Sponsor: Fudan University
Summary
This study aims to evaluate and report the clinical adverse events and dosimetric parameters in breast cancer patients undergoing an "all-in-one (AIO)" one-stop, fully automated radiotherapy workflow. By systematically tracking these clinical and physical metrics, we seek to establish a standardized clinical protocol for AIO radiotherapy in breast cancer management.
Official title: Application of CT-Linac-Based "All-in-One" One-Stop Radiotherapy in All-Scenario Breast Cancer Radiotherapy: A Prospective Clinical Study
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
225
Start Date
2021-08-27
Completion Date
2028-11-27
Last Updated
2026-08-20
Healthy Volunteers
No
Conditions
Interventions
Radiotherapy procedure
The workflow relies on specialized convolutional neural networks for automated segmentation and dose-prediction auto-planning. These breast cancer models were trained on 285 historical institutional cases spanning radical mastectomy and breast-conserving surgery over five years. Auto-delineated structures include the clinical target volume, regional lymph nodes (if involved), tumor bed (identified by surgical clips), heart, bilateral lungs, unaffected breast, spinal cord, esophagus, thyroid, and affected humeral head. These contours guide dose prediction to generate deliverable tangential arc plans via clinical-goal-guided automated optimization in the treatment planning system. To adapt to the on-couch treatment scenario, models were validated on retrospective data and offline routines to maximize target delineation accuracy and the first-approval rate of auto-plans.
Locations (1)
Fudan University Shanghai Cancer Center
Shanghai, Shanghai Municipality, China