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Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01): a case records based retrospective study
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注册号:

Registration number:

ChiCTR1900024020 

最近更新日期:

Date of Last Refreshed on:

2019-06-22 

注册时间:

Date of Registration:

2019-06-22 

注册号状态:

预注册  

Registration Status:

Prospective registration  

注册题目:

深度学习乳腺癌磁共振影像组学预测淋巴结转移及预后 (RBC-01) 

Public title:

Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01): a case records based retrospective study 

注册题目简写:

 

English Acronym:

 

研究课题的正式科学名称:

深度学习乳腺癌磁共振影像组学预测淋巴结转移及预后(RBC-01) 

Scientific title:

Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01): a case records based retrospective study 

研究课题代号(代码):

Study subject ID:

RBC-01 

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:

 

申请注册联系人:

余运芳 

研究负责人:

姚和瑞 

Applicant:

Yunfang Yu 

Study leader:

Herui Yao 

申请注册联系人电话:

Applicant telephone:

+86 13660238987 

研究负责人电话:

Study leader's telephone:

+86 13500018020 

申请注册联系人传真 :

Applicant Fax:

 

研究负责人传真:

Study leader's fax:

 

申请注册联系人电子邮件:

Applicant E-mail:

yuyf9@mail.sysu.edu.cn 

研究负责人电子邮件:

Study leader's E-mail:

yaoherui@mail.sysu.edu.cn 

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):

 

申请注册联系人通讯地址:

广东省广州市越秀区沿江西路107号 

研究负责人通讯地址:

广东省广州市越秀区沿江西路107号 

Applicant address:

107 Yanjiang Road West, Yuexiu District, Guangzhou, Guangdong, China 

Study leader's address:

107 Yanjiang Road West, Yuexiu District, Guangzhou, Guangdong, China 

申请注册联系人邮政编码:

Applicant postcode:

510000 

研究负责人邮政编码:

Study leader's postcode:

510000 

申请人所在单位:

中山大学孙逸仙纪念医院 

Applicant's institution:

Sun Yat-Sen Memorial Hospital, Sun Yat-sen University 

是否获伦理委员会批准:

是 

Approved by ethic committee:

Yes 

伦理委员会批件文号:

Approved No. of ethic committee:

SYSEC-KY-KS-2019-054 

伦理委员会批件附件:

Approved file of Ethical Committee:

查看附件View

批准本研究的伦理委员会名称:

中山大学孙逸仙纪念医院医学伦理委员会 

Name of the ethic committee:

Ethics committee of Sun Yat-sen Memorial Hospital 

伦理委员会批准日期:

Date of approved by ethic committee:

2019-05-28 

伦理委员会联系人:

林双秀 

Contact Name of the ethic committee:

Shuangxiu Lin 

伦理委员会联系地址:

广东省广州市越秀区沿江西路107号 

Contact Address of the ethic committee:

107 Yanjiang Road West, Yuexiu District, Guangzhou, Guangdong, China 

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:

 

研究实施负责(组长)单位:

中山大学孙逸仙纪念医院 

Primary sponsor:

Sun Yat-Sen Memorial Hospital, Sun Yat-sen University 

研究实施负责(组长)单位地址:

广东省广州市越秀区沿江西路107号 

Primary sponsor's address:

107 Yanjiang Road West, Yuexiu District, Guangzhou, Guangdong, China 

试验主办单位(项目批准或申办者):

Secondary sponsor:

国家:

中国

省(直辖市):

广东

市(区县):

广州

Country:

China

Province:

Guangdong

City:

Guangzhou

单位(医院):

中山大学孙逸仙纪念医院

具体地址:

广东省广州市越秀区沿江西路107号

Institution
hospital:

Sun Yat-Sen Memorial Hospital, Sun Yat-sen University

Address:

107 Yanjiang Road West, Yuexiu District, Guangzhou, Guangdong, China

经费或物资来源:

中山大学孙逸仙纪念医院 

Source(s) of funding:

Sun Yat-Sen Memorial Hospital, Sun Yat-sen University 

研究疾病:

乳腺癌 

Target disease:

breast cancer 

研究疾病代码:

 

Target disease code:

 

研究类型:

诊断试验 

Study type:

Diagnostic test 

研究所处阶段:

其它 

Study phase:

N/A 

研究目的:

通过机器算法深度学习早期乳腺癌患者核磁共振图像及临床特征预测淋巴结转移风险,同时研究乳腺癌及癌旁的影像特征与肿瘤免疫微环境、无疾病生存时间(DFS)、总生存期(OS)的关系,更进一步建立基于机器算法深度学习乳腺癌磁共振影像组学的多组学临床预测模型预测早期乳腺癌淋巴结转移的风险。 

Objectives of Study:

This study is aim to predict lymph node metastasis via deep learning algorithms in breast cancer MRI radiomics, and study the correlation between breast cancer, tissues adjacent to breast cancer and tumor microenvironment, DFS, and OS. Furthermore, building up an clinical model to predict the risk of axillary lymph node metastasis for early stage breast cancer based on deep learning algorithms in breast cancer MRI radiomics. 

药物成份或治疗方案详述:

 

Description for medicine or protocol of treatment in detail:

 

研究设计:

连续入组 

Study design:

Sequential 

纳入标准:

1、女性 2、年龄18-75岁 3、2008年-2018原发灶病理确诊为浸润性或浸润性为主的乳腺癌 4、可以有局部淋巴结转移,但无远处脏器转移 5、术前完善乳腺MRI检查 6、接受乳腺手术及淋巴病理活检 7、ECOG-PS 0-2 

Inclusion criteria

1. Female aged from 18 to 75 years; 2. From 2008 to 2018, the primary lesion was diagnosed as invasive breast cancer; 3. Patients can have regional lymph node metastasis,but no distant organ metastasis; 4. Complete the breast MRI examination before operation; 5. Accept breast cancer surgery or lymph node biopsy; 6. ECOG-PS 0-2. 

排除标准:

1、炎性乳腺癌 2、同时伴有其它原发恶性肿瘤 3、完善乳腺MRI检查前已行手术、放疗及淋巴结活检 4、新辅助治疗后 5、远处及对侧腋窝淋巴结转移 6、广泛导管原位癌为主的乳腺癌 

Exclusion criteria:

1. Inflammatory breast cancer; 2. Accompanied with other primary malignant tumors; 3. Perform surgery,radiotherapy and lymph node biopsy before breast MRI examination; 4. Patients who have neoadjuvant chemotherapy; 5. Patients had distant and contralateral axillary lymph node metastasis; 6. The pathologic diagnosis was extensive ductal carcinoma in situ. 

研究实施时间:

Study execute time:

From2019-06-20To 2020-06-19 

征募观察对象时间:

Recruiting time:

From2019-06-20To 2024-06-19 

诊断措施:

Diagnostic measures:

金标准或参考标准(即可准确诊断某疾病的单项方法或多项联合方法,在本研究中用于诊断是否有该病的临床参考标准):

临床结局

Gold Standard or Reference Standard (The clinical reference standards required to establish the presence or absence of the target condition in the tested population in present study):

Clinical outcome

指标试验(即本研究的待评估诊断试验,无论为方法、生物标志物或设备,均请列出名称):

深度学习多组学临床预测模型

Index test:

Deep Learning Algorithms prediction model

目标人群(可以是某种疾病患者或正常人群,详细描述其疾病特征,注意应纳入符合分布特点的全序列病例,具有良好的代表性)

浸润性或浸润性为主的乳腺癌

例数:

Sample size:

1500

Target condition (The target condition is a particular disease or disease stage that the index test will be intended to identify. Please specify the characteristics in detail; the population should has a complete spectrum and good representative):

invasive breast cancer patients

容易混淆的疾病人群(即与目标疾病不易区分的一种或多种不同疾病,应避免采用正常人群对照的病例-对照设计):

例数:

Sample size:

0

Population with condition difficult to distinguish from the target condition, the normal population in a case-control study design should be avoid:

研究实施地点:

Countries of recruitment and research settings:

国家:

中国 

省(直辖市):

广东 

市(区县):

广州 

Country:

China 

Province:

Guangdong 

City:

Guangzhou 

单位(医院):

中山大学孙逸仙纪念医院 

单位级别:

三等甲级医院 

Institution
hospital:

Sun Yat-Sen Memorial Hospital,Sun Yat-sen University  

Level of the institution:

Tertiary A Hospital 

国家:

中国 

省(直辖市):

广东 

市(区县):

佛山 

Country:

China 

Province:

Guangdong 

City:

Foshan 

单位(医院):

南方医科大学顺德医院 

单位级别:

三等甲级医院 

Institution
hospital:

Shunde hospital of southern medical university  

Level of the institution:

Tertiary A Hospital 

国家:

中国 

省(直辖市):

广东 

市(区县):

东莞 

Country:

China 

Province:

Guangdong 

City:

Dongguan 

单位(医院):

中山大学附属东华医院 

单位级别:

三等甲级医院 

Institution
hospital:

Tungwah Hospital of Sun Yat-Sen University  

Level of the institution:

Tertiary A Hospital 

国家:

中国 

省(直辖市):

广东 

市(区县):

广州 

Country:

China 

Province:

Guangdong 

City:

Guangzhou 

单位(医院):

中山大学肿瘤防治中心 

单位级别:

三等甲级 

Institution
hospital:

Sun Yat-sen University Cancer Center  

Level of the institution:

Tertiary A Hospital 

测量指标:

Outcomes:

指标中文名:

有无腋窝淋巴结转移

指标类型:

主要指标 

Outcome:

Axillary lymph node metastasis

Type:

Primary indicator 

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

影像组学特征与肿瘤微环境相关性

指标类型:

次要指标 

Outcome:

The correlation of radiomics features and tumor microenvironment

Type:

Secondary indicator 

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

无疾病生存时间

指标类型:

次要指标 

Outcome:

Disease-free survival

Type:

Secondary indicator 

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

指标中文名:

总生存期

指标类型:

次要指标 

Outcome:

Overall survival

Type:

Secondary indicator 

测量时间点:

测量方法:

Measure time point of outcome:

Measure method:

采集人体标本:

Collecting sample(s)
from participants:

征募研究对象情况:

Recruiting status:

尚未开始

Not yet recruiting

年龄范围:

Participant age:

最小 Min age 18 years
最大 Max age 75 years

性别:

女性

Gender:

Female

随机方法(请说明由何人用什么方法产生随机序列):

非随机对照试验

Randomization Procedure (please state who generates the random number sequence and by what method):

N/A

盲法:

N/A

Blinding:

N/A

是否共享原始数据:

IPD sharing

Yes

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):

由医院统一管理

The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url):

Unified management by hospitals

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:

使用病例记录表及电子采集和管理系统详细记录患者基本信息

Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture:

Use the case record form and electronic data capture to record patients' personal information

数据与安全监察委员会:

Data and Safety Monitoring Committee:

有/Yes

注册人:

Name of Registration:

 2019-06-22
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