Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)
1 other identifier
observational
1,500
1 country
5
Brief Summary
This bi-directional, multicentre study aims to assess multiparametric MRI Radiomics-based prediction model for identifying metastasis lymph nodes and prognostic prediction in breast cancer.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started May 2019
Longer than P75 for all trials
5 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
May 28, 2019
CompletedFirst Submitted
Initial submission to the registry
June 26, 2019
CompletedFirst Posted
Study publicly available on registry
July 1, 2019
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 31, 2020
CompletedStudy Completion
Last participant's last visit for all outcomes
January 1, 2025
CompletedAugust 15, 2019
August 1, 2019
1 year
June 26, 2019
August 13, 2019
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Disease free survival (DFS)
Disease free survival (DFS), which defined as the time from the diagnosis of breast cancer to the confirmed time of metastatic disease, or death due to any other cause.
5 years
Secondary Outcomes (5)
The correlation of radiomics features and tumor microenvironment
baseline (Completed MRI data before biopsy,surgery,neoadjuvant and radiotherapy.)
Lymph node metastasis
Baseline
Overall survival (OS)
5 years
Beast cancer specific motality (BCSM)
5 years
Recurrence free survival (RFS)
5 years
Study Arms (4)
Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
The cohort of Sun Yat-Sen Memorial Hospital of Sun Yat-sen University is a training cohort.
Sun Yat-sen University Cancer Center
The cohort of Sun Yat-sen University Cancer Center is a validation cohort.
Tungwah Hospital of Sun Yat-Sen University
The cohort of Tungwah Hospital of Sun Yat-Sen University is a validation cohort.
Shunde hospital of southern medical university
The cohort of Shunde hospital of southern medical university is a validation cohort.
Interventions
As this is a patient registry, there are no interventions.
Eligibility Criteria
Patients who had early stage breast cancer and completed the breast MRI examination before operation,lymph node biopsy,neoadjuvant chemotherapy,and radiotherapy.
You may qualify if:
- The primary lesion was diagnosed as invasive breast cancer
- Patients can have regional lymph node metastasis,but no distant organ metastasis
- Complete the breast MRI examination before treatment
- Accept breast cancer surgery or lymph node biopsy
- Eastern Cooperative Oncology Group performance status 0-2
You may not qualify if:
- Inflammatory breast cancer
- Accompanied with other primary malignant tumors
- Perform surgery,radiotherapy and lymph node biopsy before breast MRI examination
- Patients who have neoadjuvant chemotherapy
- Patients had distant and contralateral axillary lymph node metastasis
- The pathologic diagnosis was extensive ductal carcinoma in situ
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen Universitylead
- Sun Yat-sen Universitycollaborator
- Tungwah Hospital of Sun Yat-Sen Universitycollaborator
- Southern Medical University, Chinacollaborator
- Zhongshan Ophthalmic Center, Sun Yat-sen Universitycollaborator
Study Sites (5)
Tungwah Hospital of Sun Yat-Sen University
Dongguan, Guangdong, 523000, China
Shunde hospital of southern medical university
Foshan, Guangdong, 528300, China
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, 510000, China
Zhongshan Ophthalmic Center, Sun Yat-Sen University
Guangzhou, Guangdong, 510000, China
Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510120, China
Related Publications (1)
Yu Y, He Z, Ouyang J, Tan Y, Chen Y, Gu Y, Mao L, Ren W, Wang J, Lin L, Wu Z, Liu J, Ou Q, Hu Q, Li A, Chen K, Li C, Lu N, Li X, Su F, Liu Q, Xie C, Yao H. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study. EBioMedicine. 2021 Jul;69:103460. doi: 10.1016/j.ebiom.2021.103460. Epub 2021 Jul 4.
PMID: 34233259DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY CHAIR
Herui Yao, PhD
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- PRINCIPAL INVESTIGATOR
Chuanmiao Xie, PhD
Sun Yat-sen University
- PRINCIPAL INVESTIGATOR
Jie Ouyang, PhD
Tungwah Hospital of Sun Yat-Sen University
- PRINCIPAL INVESTIGATOR
Qiugen Hu, PhD
Southern Medical University, China
- PRINCIPAL INVESTIGATOR
Haotian Lin, PhD
Zhongshan Ophthalmic Center, Sun Yat-sen University
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Principal Investigator
Study Record Dates
First Submitted
June 26, 2019
First Posted
July 1, 2019
Study Start
May 28, 2019
Primary Completion
May 31, 2020
Study Completion
January 1, 2025
Last Updated
August 15, 2019
Record last verified: 2019-08
Data Sharing
- IPD Sharing
- Will not share
Requests for the individual data or study documents will be considered where the proposed use aligns with public good purposes, does not conflict with other requests, and the requestor is willing to sign a data access agreement. Contact is though the corresponding author.