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注册号: Registration number: |
ChiCTR2500105600 |
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最近更新日期: Date of Last Refreshed on: |
2025-07-07 17:19:03 |
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注册时间: Date of Registration: |
2025-07-07 00:00:00 |
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注册号状态: |
补注册 |
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Registration Status: |
Retrospective registration |
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注册题目: |
基于可解释性人工智能的乳腺癌新辅助治疗效果预测 |
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Public title: |
Prediction of Neoadjuvant Therapy Response in Breast Cancer Based on Interpretable Artificial Intelligence |
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注册题目简写: |
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English Acronym: |
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研究课题的正式科学名称: |
基于可解释性人工智能的乳腺癌新辅助治疗效果预测 |
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Scientific title: |
Prediction of Neoadjuvant Therapy Response in Breast Cancer Based on Interpretable Artificial Intelligence |
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研究课题代号(代码): Study subject ID: |
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在二级注册机构或其它机构的注册号: The registration number of the Partner Registry or other register: |
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申请注册联系人: |
邓立彬 |
研究负责人: |
周瑶 |
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Applicant: |
Libin Deng |
Study leader: |
Yao Zhou |
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申请注册联系人电话: Applicant telephone: |
+86 151 7040 1580 |
研究负责人电话:
Study leader's |
+86 138 7916 8691 |
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申请注册联系人传真 : Applicant Fax: |
研究负责人传真: Study leader's fax: |
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申请注册联系人电子邮件: Applicant E-mail: |
lbdeng@ncu.edu.cn |
研究负责人电子邮件: Study leader's E-mail: |
zhouyaoican@163.com |
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申请单位网址(自愿提供): Applicant website(voluntary supply): |
研究负责人网址(自愿提供): Study leader's website(voluntary supply): |
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申请注册联系人通讯地址: |
南昌市人民医院, 南昌市象山南路2号 |
研究负责人通讯地址: |
南昌市人民医院, 南昌市象山南路2号 |
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Applicant address: |
Nanchang People's Hospital, No. 2 Xiangshan South Road, Nanchang Third Hospital, Nanchang |
Study leader's address: |
Nanchang People's Hospital, No. 2 Xiangshan South Road, Nanchang |
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申请注册联系人邮政编码: Applicant postcode: |
研究负责人邮政编码: Study leader's postcode: |
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申请人所在单位: |
南昌市人民医院 |
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Applicant's institution: |
Nanchang People's Hospital |
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研究负责人所在单位: |
南昌市人民医院 |
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Affiliation of the Leader: |
Nanchang People's Hospital |
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是否获伦理委员会批准: |
是 |
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Approved by ethic committee: |
Yes |
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伦理委员会批件文号: Approved No. of ethic committee: |
K-kt2025026 |
伦理委员会批件附件: Approved file of Ethical Committee: |
查看附件View |
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批准本研究的伦理委员会名称: |
南昌市人民医院(南昌市第三医院)伦理委员会 |
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Name of the ethic committee: |
Nanchang People's Hospital (Nanchang Third Hospital) of Ethics Committee |
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伦理委员会批准日期: Date of approved by ethic committee: |
2025-02-21 00:00:00 | ||
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伦理委员会联系人: |
郭飘飘 |
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Contact Name of the ethic committee: |
Piaopiao Guo |
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伦理委员会联系地址: |
南昌市西湖区九州大街1268号急诊三楼伦理委员会办公室 |
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Contact Address of the ethic committee: |
Ethics Committee Office, 3rd Floor Emergency Department, No. 1268, Jiuzhou Street, Xihu District, Nanchang City |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
+86 139 7085 5218 |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
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研究实施负责(组长)单位: |
南昌市人民医院 |
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Primary sponsor: |
Nanchang People's Hospital |
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研究实施负责(组长)单位地址: |
南昌市象山南路2号南昌市人民医院 |
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Primary sponsor's address: |
No. 2 Xiangshan South Road, Nanchang People's Hospital |
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试验主办单位(项目批准或申办者): Secondary sponsor: |
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经费或物资来源: |
这项工作是自筹项目。 |
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Source(s) of funding: |
This project is self-funded. |
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研究疾病: |
乳腺癌 |
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Target disease: |
Breast cancer |
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研究疾病代码: |
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Target disease code: |
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研究类型: |
观察性研究 |
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Study type: |
Observational study |
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研究所处阶段: |
回顾性研究 | ||||||||||||||||||||||
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Study phase: |
Retrospective study |
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研究设计: |
队列研究 |
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Study design: |
Cohort study |
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研究目的: |
本项目致力于构建乳腺癌新辅助治疗的多模态数据集,通过回顾性收集乳腺癌新辅助患者的多模态数据,重点涵盖病理切片(WSI)和RCS评分等关键医疗数据。我们将运用人工智能算法对病理切片WSI进行深度处理,包括图像特征的提取与分类,以挖掘其中蕴含的潜在信息。在此基础上,利用病理图像数据构建、训练并验证智能模型,确保其准确性和可靠性。最终,我们将整合病理与临床数据,构建一个基于人工智能(AI)的乳腺癌病理切片疗效预测模型,旨在开发出能够精准预测乳腺癌新辅助治疗疗效的AI模型,为临床治疗提供有力支持。 |
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Objectives of Study: |
This project is committed to constructing a multimodal dataset for neoadjuvant breast cancer treatment. By retrospectively collecting multimodal data from neoadjuvant breast cancer patients, we focus on key medical data such as whole slide images (WSI) of pathological sections and RCS scores. We will employ advanced artificial intelligence algorithms to conduct in-depth processing of the pathological WSI, including the extraction and classification of image features, to uncover the potential information contained within. On this basis, we will construct, train, and validate intelligent models using pathological image data to ensure their accuracy and reliability. Finally, by integrating pathological and clinical data, we will build an artificial intelligence (AI)-based efficacy prediction model for breast cancer pathological sections, aiming to develop an AI model that can accurately predict the efficacy of neoadjuvant breast cancer treatment and provide strong support for clinical treatment. |
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药物成份或治疗方案详述: |
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Description for medicine or protocol of treatment in detail: |
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纳入标准: |
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Inclusion criteria |
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排除标准: |
(1)合并其他恶性肿瘤;(2)其他治疗方式的乳腺癌;(3)临床或病理数据不完整。 |
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Exclusion criteria: |
(1) Combined with other malignancies; (2) Breast cancer treated by other methods; (3) Incomplete clinical or pathological data |
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研究实施时间: Study execute time: |
从 From 2024-02-21 00:00:00至 To 2025-02-21 00:00:00 |
征募观察对象时间: Recruiting time: |
从 From 2024-02-21 00:00:00 至 To 2025-02-21 00:00:00 |
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干预措施: Interventions: |
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研究实施地点: Countries of recruitment and research settings: |
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测量指标: Outcomes: |
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采集人体标本:
Collecting sample(s)
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征募研究对象情况: Recruiting status: |
结束 /Completed |
年龄范围: Participant age: |
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性别: |
男女均可 |
Gender: |
Both |
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随机方法(请说明由何人用什么方法产生随机序列): |
无 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
None |
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是否公开试验完成后的统计结果: Calculated Results after the Study Completed public access: |
不公开/Private |
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盲法: |
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Blinding: |
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是否共享原始数据: IPD sharing |
是Yes |
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共享原始数据的方式(说明:请填入公开原始数据日期和方式,如采用网络平台,需填该网络平台名称和网址): |
邮箱,2026年10月后,邮件申请方式 |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
E-mail, after October 2026, e-mail application method |
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数据采集和管理(说明:数据采集和管理由两部分组成,一为病例记录表(Case Record Form, CRF),二为电子采集和管理系统(Electronic Data Capture, EDC),如ResMan即为一种基于互联网的EDC: |
通过南昌市人民医院的电子病理采集管理系统获取数据 |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
Data was obtained through the electronic pathology collection management system of Nanchang People's Hospital. |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
暂未确定/Not yet |