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审核状态: Project audit state: |
通过审核 Successful |
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注册号: Registration number: |
ChiCTR2100043655 |
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最近更新日期: Date of Last Refreshed on: |
2021-02-24 13:24:13 |
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注册时间: Date of Registration: |
2021-02-24 00:00:00 |
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注册号状态: |
预注册 |
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Registration Status: |
Prospective registration |
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注册题目: |
乳腺疾病超声人工智能诊断平台的构建及有效性检验 |
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Public title: |
Construction and validity testing of an ultrasound artificial intelligence diagnosis platform for breast diseases |
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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: |
乳腺疾病超声人工智能诊断平台的构建及有效性检验 |
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Scientific title: |
Construction and validity testing of an ultrasound artificial intelligence diagnosis platform for breast diseases |
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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: |
韩春光 |
研究负责人: |
裴静 |
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Applicant: |
Chunguang Han |
Study leader: |
Jing Pei |
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申请注册联系人电话: Applicant telephone: |
18856038092 |
研究负责人电话:
Study leader's |
13966668272 |
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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: |
1255098726@qq.com |
研究负责人电子邮件: Study leader's E-mail: |
peijing@ahmu.edu.cn |
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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: |
安徽省合肥市绩溪路218号 |
研究负责人通讯地址: |
安徽省合肥市绩溪路218号 |
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Applicant address: |
218 Jixi Road, Hefei, Anhui, China |
Study leader's address: |
218 Jixi Road, Hefei, Anhui, China |
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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: |
安徽医科大学第一附属医院 |
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Applicant's institution: |
The First Affiliated Hospital of Anhui Medical University |
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研究负责人所在单位: |
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Affiliation of the Leader: |
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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: |
安医一附院伦审-PJ2017-11-05 |
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: |
安徽医科大学第一附属医院临床医学研究伦理委员会 |
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Name of the ethic committee: |
Clinical Medical Research Ethics Committee of the First Affiliated Hospital of Anhui Medical University |
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伦理委员会批准日期: Date of approved by ethic committee: |
2017-11-04 00:00:00 | ||
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伦理委员会联系人: |
王晓虎 |
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Contact Name of the ethic committee: |
Xiaohu Wang |
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伦理委员会联系地址: |
安徽医科大学第一附属医院行政楼6楼临床医学研究伦理委员会办公室 |
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Contact Address of the ethic committee: |
Clinical Medical Research Ethics Committee Office, 6th Floor, Administrative Building, The First Affiliated Hospital of Anhui Medical University, 218 Jixi Road, Hefei, Anhui, China |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
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: |
安徽医科大学第一附属医院 |
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Primary sponsor: |
The First Affiliated Hospital of Anhui Medical University |
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研究实施负责(组长)单位地址: |
安徽省合肥市绩溪路218号 |
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Primary sponsor's address: |
218 Jixi Road, Hefei, Anhui, China |
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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: |
自筹 |
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Source(s) of funding: |
self-raised |
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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: |
诊断试验 |
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Study type: |
Diagnostic test |
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研究所处阶段: |
其它 | ||||||||||||||||||||||
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Study phase: |
N/A |
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研究设计: |
连续入组 |
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Study design: |
Sequential |
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研究目的: |
乳腺癌发病率居女性新发恶性肿瘤的首位,并呈逐年上升趋势。早期发现,早期诊断和早期治疗是提高其治愈率的关键。超声为我国乳腺癌筛查的主要方式。然而,高水平超声医师资源的稀缺,不仅造成了医疗资源城乡分配不均、下级市县女性对高水平筛查资源的可及性差,而且导致大部分医生常年超负荷工作,机械繁琐的人工阅片难免会造成误诊与漏诊。导致大部分患者在被发现时已是中、晚期阶段。 因此开发出基于乳腺超声影像的高水平的人工智能诊断平台,构建人工智能分级诊断网络,对于弥补目前有限的筛查资源,推动国家分级诊疗政策的落实以及提高偏远地区医疗资源的可及性具有巨大的应用价值和市场潜力。 同时基于现有研究基础,我们提出了“基于乳腺超声的多模态-多指标-多视角的立体化评估系统(MMM-SAS)”的理念,即联合灰阶超声、多普勒超声及弹性超声等多种模态的超声技术,综合考虑定量、定性和影像组学等多重指标以及多个切面的立体化的影像学信息,从而对真实世界中病灶的病理组织学信息及生物学行为甚至是治疗疗效和预后生存有更加精准的预测和诊断能力。本研究旨在评估基于乳腺超声的多模态-多指标-多视角的立体化评估系统(MMM-SAS)用于乳腺病灶恶性风险评估的诊断效能,以及在改善传统BI-RADS分类,减少不必要的活检和减少漏诊、误诊的临床应用价值。 同时,乳腺超声与钼靶、超声造影、磁共振等影像学检查,乳管镜,以及FNA、CNB、VAB和开放活检等活检技术的结合将被进一步的研究,以评估基于多种检查的立体化评估系统用于乳腺病灶恶性风险评估的诊断效能 ,以及对新辅助化疗疗效、腋窝淋巴结转移情况,以及乳腺癌患者预后的预测能力。另外,基于乳腺超声并联合其他临床检查信息的人工智能诊断技术在其他方面的预测和预后应用也将进一步被研究,以丰富该乳腺疾病超声人工智能诊断平台的临床应用范围和价值。 |
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Objectives of Study: |
The incidence of breast cancer ranks first among new malignant tumors in women and is on the rise year by year. Early detection, early diagnosis, and early treatment are the keys to improve its cure rate. Ultrasound is the main modality of breast cancer screening in China. However, the scarcity of high-level ultrasound physician resources has not only caused an uneven distribution of medical resources between urban and rural areas and poor accessibility to high-level screening resources for women in lower cities and counties, but also led to most physicians being overworked all year round, and mechanical and tedious manual reading of films inevitably leads to misdiagnosis and missed diagnosis. As a result, most patients are already in the middle or late stage when they are detected. Therefore, the development of a high-level AI diagnostic platform based on breast ultrasound images and the construction of an AI hierarchical diagnostic network have great application value and market potential to make up for the current limited screening resources, promote the implementation of the national policy of hierarchical diagnosis and treatment, and improve the accessibility of medical resources in remote areas. Based on the existing research, we put forward the concept of "Multi-modal Multi-indicator Multi-view Stereoscopic Assessment System (MMM-SAS)", that is, the combination of gray-scale ultrasound, Doppler ultrasound, and elastic ultrasound, comprehensively considering multiple indicators such as quantitative, qualitative, and imaging, as well as three-dimensional imaging information of multiple sections. Thus, it has a more accurate ability to predict and diagnose the histopathological information and biological behavior of the lesions in the real world, even the therapeutic effect, and prognosis. The purpose of this study was to evaluate the diagnostic efficacy of breast ultrasound-based Multi-modal Multi-indicator Multi-view Stereoscopic Assessment System (MMM-SAS) for malignant risk assessment of breast lesions and to improve the traditional BI-RADS classification, reduce unnecessary biopsies and reduce missed diagnosis and misdiagnosis. The combination of breast ultrasound with imaging examinations such as mammography, ultrasonography, MRI, breast ductoscopy, and biopsy techniques such as FNA, CNB, VAB, and open biopsy will be further investigated to assess the diagnostic efficacy of a stereoscopic assessment system based on multiple examinations for malignancy risk assessment of breast lesions, as well as for neoadjuvant chemotherapy efficacy, axillary lymph node metastasis, and breast cancer patient In addition, other predictive and prognostic applications of AI technology based on breast ultrasound and combined with other clinical examination information will be further investigated to enrich the clinical application and value of this breast disease ultrasound AI platform. |
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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: |
1.超声、钼靶或磁共振等任意一种检查显示有乳腺肿块的患者,特别是BI-RADS3类和4类患者; 2.知情此次研究,并签订知情同意书。自愿进行剪切波弹性成像检查; 3.同意并配合穿刺活检、旋切活检或手术治疗,最终获得病理诊断。 |
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Inclusion criteria |
(1) Patients with breast masses on examinations such as ultrasound, mammography, or MRI of any kind, especially BI-RADS category 3 and 4 patients; (2) informed of this study and signed an informed consent form. Voluntary shear wave elastography examination; (3) agreed and combined with puncture biopsy, rotary biopsy, or surgical treatment, and finally obtained the pathological diagnosis. |
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排除标准: |
(1)妊娠期或哺乳期; (2)乳腺内有假体植入物; (3)患侧有外科手术史; (4)恶性肿瘤病史及其他恶病质患者。 |
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Exclusion criteria: |
(1) pregnancy or lactation; (2) implants in the breast; (3) history of surgery on the affected side; (4) history of malignant tumor and other cachexia. |
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研究实施时间: Study execute time: |
从 From 2021-03-01 00:00:00至 To 2026-12-31 00:00:00 |
征募观察对象时间: Recruiting time: |
从 From 2021-03-01 00:00:00 至 To 2026-12-31 00:00:00 |
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诊断试验: Diagnostic Tests: |
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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: |
尚未开始 Not yet recruiting |
年龄范围: Participant age: |
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性别: |
女性 |
Gender: |
Female |
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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): |
不适用 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
N/A |
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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: |
公开/Public |
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盲法: |
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Blinding: |
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试验完成后的统计结果(上传文件): |
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Calculated Results after
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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): |
试验完成后,面对合理的请求进行共享 |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
After the experiment completed, it will be shared with reasonable requests. |
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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: |
CRF |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
CRF |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
有/Yes |