Application of CT-Linac-Based "All-in-One" One-Stop Radiotherapy in Breast Cancer
2026年8月19日 更新者：Xiaoli Yu、Fudan University
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.
研究概览
研究类型
介入性
注册 (估计的)
225
阶段
- 不适用
联系人和位置
本节提供了进行研究的人员的详细联系信息，以及有关进行该研究的地点的信息。
学习联系方式
- 姓名：Xiaoli Yu, MD, PhD
- 电话号码：+86-021-64175590
- 邮箱：xiaoliyu@fudan.edu.cn
研究联系人备份
- 姓名：Xiaofang Wang, MD, PhD
- 电话号码：+86 18017317247
- 邮箱：xiaofang0708@yeah.net
学习地点
-
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Shanghai Municipality
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Shanghai、Shanghai Municipality、中国、200032
- 招聘中
- Fudan University Shanghai Cancer Center
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接触：
- Xiaofang Wang Wang
- 电话号码：+86 18017317247
- 邮箱：xiaofang0708@yeah.net
-
-
参与标准
研究人员寻找符合特定描述的人，称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。
资格标准
适合学习的年龄
- 成人
- 年长者
接受健康志愿者
不
描述
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
学习计划
本节提供研究计划的详细信息，包括研究的设计方式和研究的衡量标准。
研究是如何设计的？
设计细节
- 主要用途：其他
- 分配：非随机化
- 介入模型：顺序分配
- 屏蔽：无（打开标签）
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
|
其他：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.
|
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.
|
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其他：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.
|
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.
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研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Acute adverse events
大体时间：6 months
|
The incidence and severity of acute adverse event include radiation dermatitis, pruritus, skin pain, radiation esophagitis, and radiation pneumonitis.
|
6 months
|
次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Accuracy
大体时间：2 months
|
Auto-segmentation accuracy was assessed by comparing automatically generated contours against the final physician-approved contours
|
2 months
|
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Success rate
大体时间：2 months
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Record AIO workflow success rate: online planning one-pass optimization success rate.
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2 months
|
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Quality of life (QoL)
大体时间：6 months
|
Quality of life will be evaluated via standardized QoL scales.
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6 months
|
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Time efficiency
大体时间：2 months
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The time efficiency of the workflow was automatically recorded by the system
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2 months
|
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Full-Workflow Patient Intrafraction Motion
大体时间：2 months
|
Evaluated based on geometric deviations between pretreatment image-guided radiotherapy (IGRT), posttreatment imaging, and the baseline simulation CT
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2 months
|
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Correlation of Patient Metrology with Setup Error and Dosimetric Performance
大体时间：2 months
|
Evaluation of how Body Mass Index (BMI) and weight fluctuations correlate with geometric setup errors and in vivo gamma pass rates
|
2 months
|
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Correlation of Anatomical Scale with Setup Error and Dosimetric Performance
大体时间：2 months
|
Evaluation of how anatomical scale/breast size correlates with geometric setup errors and in vivo gamma pass rates
|
2 months
|
合作者和调查者
在这里您可以找到参与这项研究的人员和组织。
研究记录日期
这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查，以确保它们在发布到公共网站之前符合特定的质量控制标准。
研究主要日期
学习开始 (实际的)
2021年8月27日
初级完成 (估计的)
2028年8月27日
研究完成 (估计的)
2028年11月27日
研究注册日期
首次提交
2026年7月1日
首先提交符合 QC 标准的
2026年8月19日
首次发布 (实际的)
2026年8月20日
研究记录更新
最后更新发布 (实际的)
2026年8月20日
上次提交的符合 QC 标准的更新
2026年8月19日
最后验证
2026年6月1日
更多信息
此信息直接从 clinicaltrials.gov 网站检索，没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求，请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改，我们的网站上也会自动更新.