An interventional study of Radiotherapy procedure in Breast Cancer, sponsored by Fudan University. Recruiting at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-08-20.
Sponsored by Fudan University · Not applicable, Interventional, and Other
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.
Exclusion Criteria:
Evaluates the feasibility, safety, and patient experiences of the AI-empowered AIO workflow in breast cancer patients undergoing whole-breast irradiation (WBI) without regional nodal involvement.
Other: Radiotherapy procedure
Evaluates the feasibility, safety, and patient experiences of the AI-empowered AIO workflow in breast cancer patients with broader radiotherapy indications, including breast/chest wall irradiation with or without regional nodal radiotherapy.
Other: 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.
Acute adverse events
The incidence and severity of acute adverse event include radiation dermatitis, pruritus, skin pain, radiation esophagitis, and radiation pneumonitis.
Time frame: 6 months
Accuracy
Auto-segmentation accuracy was assessed by comparing automatically generated contours against the final physician-approved contours
Time frame: 2 months
Success rate
Record AIO workflow success rate: online planning one-pass optimization success rate.
Time frame: 2 months
Quality of life (QoL)
Quality of life will be evaluated via standardized QoL scales.
Time frame: 6 months
Time efficiency
The time efficiency of the workflow was automatically recorded by the system
Time frame: 2 months
Full-Workflow Patient Intrafraction Motion
Evaluated based on geometric deviations between pretreatment image-guided radiotherapy (IGRT), posttreatment imaging, and the baseline simulation CT
Time frame: 2 months
Correlation of Patient Metrology with Setup Error and Dosimetric Performance
Evaluation of how Body Mass Index (BMI) and weight fluctuations correlate with geometric setup errors and in vivo gamma pass rates
Time frame: 2 months
Correlation of Anatomical Scale with Setup Error and Dosimetric Performance
Evaluation of how anatomical scale/breast size correlates with geometric setup errors and in vivo gamma pass rates
Time frame: 2 months
Plan to share: Yes
Supporting information: Study protocol, Icf
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Fudan University