Breast Neoplasm FemaleNCT04003558
Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)
An observational study of Breast Neoplasm Female and Early-stage Breast Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 5 sites in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 75 years, women only
- Conditions the study is for
- Breast Neoplasm Female, Early-stage Breast Cancer, Radiomics
- How many people
- 1,500 participants
- Where
- 5 sitesChina
- Study dates
- 28 May 2019to 1 Jan 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 75 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Patients who had early stage breast cancer and completed the breast MRI examination before operation,lymph node biopsy,neoadjuvant chemotherapy,and radiotherapy.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
China5 sites, 5 recruiting
- Not yet recruiting
Tungwah Hospital of Sun Yat-Sen University
Dongguan, Guangdong, 523000
Jie Ouyang, PhD, +8613537479470, kitty865@163.com
Jie Ouyang, PhD
- Recruiting
Shunde hospital of southern medical university
Foshan, Guangdong, 528300
Qiugen Hu, PhD, +8613928206009, hu6009@163.com
Qiugen Hu, PhD
- Not yet recruiting
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, 510000
Chuanmiao Xie, PhD, +8618903050011, xiechm@sysucc.org.cn
Chuanmiao Xie, PhD
- Not yet recruiting
Zhongshan Ophthalmic Center, Sun Yat-Sen University
Guangzhou, Guangdong, 510000
Haotian Lin, PhD, +8613802793086, gddlht@aliyun.com
Wenben Chen, MD, +8618819472798, Weberchan@foxmail.com
- Recruiting
Sun Yat-Sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510120
Herui Yao, PhD, +8613500018020, yaoherui@mail.sysu.edu.cn
Herui Yao, PhD
About this study
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.
Read the detailed description
Sensitivity for prediction of lymph node metastasis and survival of currently available prognostic scores in limited. This study proposes to establish a deep learning algorithms of multiparametric MRI radiomics and nomogram for identifying lymph node metastasis and prognostic prediction of breast cancer. The study will investigate the relationship between the radiomics and the tumor microenvironment. The study includes the construction of multiparametric MRI radiomics-based prediction model and the validation of the prediction model.
- Conditions
- Breast Neoplasm Female, Early-stage Breast Cancer, Radiomics, Axillary Lymph Node, Survival, Prosthesis
- Being tested
- No interventions
- Keywords
- Early-stage Breast Cancer, Radiomics, Axillary lymph node metastasis, Tumor microenvironment, Survival, Deep learning
- Medical subject headings
- Breast Neoplasms, Prosthesis Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionsOther
As this is a patient registry, there are no interventions.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: No interventions | |
| Sun Yat-sen University Cancer Center | The cohort of Sun Yat-sen University Cancer Center is a validation cohort. Receives: Other: No interventions | |
| Tungwah Hospital of Sun Yat-Sen University | The cohort of Tungwah Hospital of Sun Yat-Sen University is a validation cohort. Receives: Other: No interventions | |
| Shunde hospital of southern medical university | The cohort of Shunde hospital of southern medical university is a validation cohort. Receives: Other: No interventions |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 5 years
Secondary outcomes (5)
The correlation of radiomics features and tumor microenvironment
Radiomics is a tool to analyze tumor microenvironment characteristics based on breast MRI images.
Measured: baseline (Completed MRI data before biopsy,surgery,neoadjuvant and radiotherapy.)
Lymph node metastasis
The value of Radiomics of multiparametric MRI in predicting axillary lymph node metastasis.
Measured: Baseline
Overall survival (OS)
The association between Radiomics of multiparametric MRI and overall survival (OS), which defined as the time from the beginning of diagnosis of breast cancer to the death with any causes.
Measured: 5 years
Beast cancer specific motality (BCSM)
Defined as time between randomization and the time of death occur specific due to breast cancer
Measured: 5 years
Recurrence free survival (RFS)
defined as time between randomization and the time of any recurrence of ipsilateral chest, breast, regional lymph node recurrence, distant metastases, or death occurred
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Jul 2019
- Study start28 May 2019
- Primary completion31 May 2020 (estimated)
- Study completion1 Jan 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Breast Neoplasm FemaleStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 1,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Sun Yat-sen University, Tungwah Hospital of Sun Yat-Sen University, Southern Medical University, China, Zhongshan Ophthalmic Center, Sun Yat-sen University
- Responsible party
- Principal investigator, Herui Yao, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Investigators
- Herui Yao, PhD, Study chair, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityChuanmiao Xie, PhD, Principal investigator, Sun Yat-sen UniversityJie Ouyang, PhD, Principal investigator, Tungwah Hospital of Sun Yat-Sen UniversityQiugen Hu, PhD, Principal investigator, Southern Medical University, ChinaHaotian Lin, PhD, Principal investigator, Zhongshan Ophthalmic Center, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04003558
- Sponsor's protocol ID
- SYSEC-KY-KS-2019-054-001
- Status last verified
- Aug 2019
- First submitted
- 26 Jun 2019
- Last updated
- 15 Aug 2019
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: 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.
Publications
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.
PubMed 34233259
Study at a glance
From the registry record, last updated by the sponsor on 15 Aug 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Algorithms for Prediction of Lymph Node Metastasis and Prognosis in Breast Cancer MRI Radiomics (RBC-01)
- ClinicalTrials.gov ID
- NCT04003558
- Acronym / sponsor ID
- SYSEC-KY-KS-2019-054-001
- Conditions
- Breast Neoplasm Female, Early-stage Breast Cancer, Radiomics, Axillary Lymph Node, Survival, Prosthesis
- Keywords
- Early-stage Breast Cancer, Radiomics, Axillary lymph node metastasis, Tumor microenvironment, Survival, Deep learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Sun Yat-sen University, Tungwah Hospital of Sun Yat-Sen University, Southern Medical University, China, Zhongshan Ophthalmic Center, Sun Yat-sen University
- Study start
- 28 May 2019 (actual)
- Primary completion
- 31 May 2020 (estimated)
- Study completion
- 1 Jan 2025 (estimated)
- First posted
- 1 Jul 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Sensitivity for prediction of lymph node metastasis and survival of currently available prognostic scores in limited. This study proposes to establish a deep learning algorithms of multiparametric MRI radiomics and nomogram for identifying lymph node metastasis and prognostic prediction of breast cancer. The study will investigate the relationship between the radiomics and the tumor microenvironment. The study includes the construction of multiparametric MRI radiomics-based prediction model and the validation of the prediction model.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Sun Yat-Sen Memorial Hospital of Sun Yat-sen University | – | Other: No interventionsThe cohort of Sun Yat-Sen Memorial Hospital of Sun Yat-sen University is a training cohort. |
| Sun Yat-sen University Cancer Center | – | Other: No interventionsThe cohort of Sun Yat-sen University Cancer Center is a validation cohort. |
| Tungwah Hospital of Sun Yat-Sen University | – | Other: No interventionsThe cohort of Tungwah Hospital of Sun Yat-Sen University is a validation cohort. |
| Shunde hospital of southern medical university | – | Other: No interventionsThe cohort of Shunde hospital of southern medical university is a validation cohort. |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who had early stage breast cancer and completed the breast MRI examination before operation,lymph node biopsy,neoadjuvant chemotherapy,and radiotherapy.
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (6 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionsAs this is a patient registry, there are no interventions. | Other | Shunde hospital of southern medical university, Sun Yat-Sen Memorial Hospital of Sun Yat-sen University, Sun Yat-sen University Cancer Center, Tungwah Hospital of Sun Yat-Sen University |
Primary outcome
Disease free survival (DFS)
Time frame: 5 years
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 secondary outcomes
The correlation of radiomics features and tumor microenvironment
Time frame: baseline (Completed MRI data before biopsy,surgery,neoadjuvant and radiotherapy.)
Lymph node metastasis
Time frame: Baseline
Overall survival (OS)
Time frame: 5 years
Beast cancer specific motality (BCSM)
Time frame: 5 years
Recurrence free survival (RFS)
Time frame: 5 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Aug 2019.
- Derived from this study: PubMed 34233259Yu 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.
- Individual participant dataSharing planned: No. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Herui Yao, PhD study chair
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Chuanmiao Xie, PhD principal investigator
Sun Yat-sen University
Jie Ouyang, PhD principal investigator
Tungwah Hospital of Sun Yat-Sen University
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Neoplasm Female and Early-stage Breast Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 5 sites in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 15 Aug 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Herui Yao, PhD (+8613500018020, yaoherui@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Disease free survival (DFS) (time frame: 5 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04003558, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Aug 2019; AzTrials last refreshed its copy on 30 Oct 2026.