Application of CT-Linac-Based "All-in-One" One-Stop Radiotherapy in Breast Cancer

Application of CT-Linac-Based "All-in-One" One-Stop Radiotherapy in All-Scenario Breast Cancer Radiotherapy: A Prospective Clinical Study

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.

Study Overview

• Status: Recruiting
• Conditions: Breast Cancer
• Intervention / Treatment: Other: Radiotherapy procedure

• Study Type: Interventional
• Enrollment (Estimated): 225
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Xiaoli Yu, MD, PhD; Phone Number: +86-021-64175590; Email: xiaoliyu@fudan.edu.cn
• Study Contact Backup: Name: Xiaofang Wang, MD, PhD; Phone Number: +86 18017317247; Email: xiaofang0708@yeah.net

Study Locations

• China
  • Shanghai Municipality
    • Shanghai, Shanghai Municipality, China, 200032
      • Recruiting
      • Fudan University Shanghai Cancer Center
      • Contact: Xiaofang Wang Wang; Phone Number: +86 18017317247; Email: xiaofang0708@yeah.net

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Histologically or pathologically confirmed breast cancer with definitive indications for radiotherapy (preoperative, postoperative, or radical)
• ECOG performance status of 0-2
• Able to remain still and supine on the treatment couch for up to 30 minutes
• Provision of signed, written informed consent
• Able to comply with daily follow-ups and blood sample collections

Exclusion Criteria:

• Palliative radiotherapy for concurrent distant metastasis
• Incomplete or ongoing chemotherapy
• Synchronous multiple primary tumors
• Current pregnancy or lactation
• Prior history of radiotherapy to the ipsilateral breast, chest wall, thorax, or regional lymph nodes
• Severe non-malignant comorbidities (e.g., cardiovascular or pulmonary diseases, systemic lupus erythematosus, scleroderma) resulting in a short life expectancy or inability to tolerate radical radiotherapy
• Inability or unlikelihood to comply with study follow-up
• Inability or unwillingness to provide written informed consent

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Other
• Allocation: Non-Randomized
• Interventional Model: Sequential Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: ARM1: AI-empowered AIO WBI; 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; 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.
Other: ARM2: Expanded-Scenario AIO RT; 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Acute adverse events	The incidence and severity of acute adverse event include radiation dermatitis, pruritus, skin pain, radiation esophagitis, and radiation pneumonitis.	6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy	Auto-segmentation accuracy was assessed by comparing automatically generated contours against the final physician-approved contours	2 months
Success rate	Record AIO workflow success rate: online planning one-pass optimization success rate.	2 months
Quality of life (QoL)	Quality of life will be evaluated via standardized QoL scales.	6 months
Time efficiency	The time efficiency of the workflow was automatically recorded by the system	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	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	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	2 months

Collaborators and Investigators

• Sponsor: Fudan University

Study record dates

Study Major Dates

• Study Start (Actual): August 27, 2021
• Primary Completion (Estimated): August 27, 2028
• Study Completion (Estimated): November 27, 2028

Study Registration Dates

• First Submitted: July 1, 2026
• First Submitted That Met QC Criteria: August 19, 2026
• First Posted (Actual): August 20, 2026

Study Record Updates

• Last Update Posted (Actual): August 20, 2026
• Last Update Submitted That Met QC Criteria: August 19, 2026
• Last Verified: June 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Skin Diseases; Breast Diseases; Skin and Connective Tissue Diseases; Breast Neoplasms
• Other Study ID Numbers: FDRT-BC029

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; ICF

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No