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

NCT07776301 | Recruiting

• 1 other identifier: FDRT-BC029
• Study Type: interventional
• Target: 225
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Breast Cancer

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (7)

• Accuracy

  2 months

• Success rate

  2 months

• Quality of life (QoL)

  6 months

• Time efficiency

  2 months

• Full-Workflow Patient Intrafraction Motion

  2 months

Study Arms (2)

ARM1: AI-empowered AIO WBI

OTHER

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

ARM2: Expanded-Scenario AIO RT

OTHER

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

Interventions

Radiotherapy procedure OTHER

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.

ARM1: AI-empowered AIO WBI; ARM2: Expanded-Scenario AIO RT

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Fudan University: lead

Study Sites (1)

Fudan University Shanghai Cancer Center

Shanghai, Shanghai Municipality, 200032, China

RECRUITING

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Xiaoli Yu, MD, PhD

CONTACT

+86-021-64175590; xiaoliyu@fudan.edu.cn

Xiaofang Wang, MD, PhD

CONTACT

+86 18017317247; xiaofang0708@yeah.net

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: SEQUENTIAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: July 1, 2026
• First Posted: August 20, 2026
• Study Start: August 27, 2021
• Primary Completion (Estimated): August 27, 2028
• Study Completion (Estimated): November 27, 2028
• Last Updated: August 20, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, ICF

Locations

CN (1)