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NCT Number: NCT07776301

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

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.

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Key information

Conditions

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

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Fudan University Shanghai Cancer Center

Shanghai, Shanghai Municipality, 200032, China

Location status: Recruiting

Location contact

Xiaofang Wang Wang

CONTACT

[email protected]

+86 18017317247

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. Acute adverse events

    Time frame: 6 months

    The incidence and severity of acute adverse event include radiation dermatitis, pruritus, skin pain, radiation esophagitis, and radiation pneumonitis.

Secondary outcomes

  1. Accuracy

    Time frame: 2 months

    Auto-segmentation accuracy was assessed by comparing automatically generated contours against the final physician-approved contours

  2. Success rate

    Time frame: 2 months

    Record AIO workflow success rate: online planning one-pass optimization success rate.

  3. Quality of life (QoL)

    Time frame: 6 months

    Quality of life will be evaluated via standardized QoL scales.

  4. Time efficiency

    Time frame: 2 months

    The time efficiency of the workflow was automatically recorded by the system

  5. Full-Workflow Patient Intrafraction Motion

    Time frame: 2 months

    Evaluated based on geometric deviations between pretreatment image-guided radiotherapy (IGRT), posttreatment imaging, and the baseline simulation CT

  6. Correlation of Patient Metrology with Setup Error and Dosimetric Performance

    Time frame: 2 months

    Evaluation of how Body Mass Index (BMI) and weight fluctuations correlate with geometric setup errors and in vivo gamma pass rates

  7. Correlation of Anatomical Scale with Setup Error and Dosimetric Performance

    Time frame: 2 months

    Evaluation of how anatomical scale/breast size correlates with geometric setup errors and in vivo gamma pass rates

Study contacts

Contact information is provided by the study sponsor or research team.

Xiaofang Wang, MD, PhD

CONTACT

[email protected]

+86 18017317247

Xiaoli Yu, MD, PhD

CONTACT

[email protected]

+86-021-64175590

Sponsors and collaborators

Lead sponsor

Fudan University

Other

Registry information

Official study title

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

Important dates

Study start
2021
Primary completion
2028
Study completion
2028
First posted
Aug 20, 2026
Registry last updated
Aug 20, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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