Breast CancerNCT06546072
Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer
An observational study of Breast Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
Trial summary
Breast cancer poses a significant global health challenge, especially among women, with high rates of recurrence and distant spread despite early interventions. The timely identification of metastasis risk and accurate prediction of treatment strategies are critical for improving prognosis. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Cancer
- What is being tested?
- MRI
- How many people
- 1,199 participants
- Where
- No sites listed yet
- Study dates
- 23 Mar 2011to 6 Dec 2021
- 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 years and older
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: In the study's initial phase, 1199 patients were randomly allocated at a ratio of 8:2 to training and testing datasets for automatic tumor segmentation. Subsequently, we randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts for DFS prediction. The remaining patients were divided into two independent external-validation cohorts: 432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1, and 198 from Dongguan Tungwah Hospital (DTH; Dongguan, China) and Shunde Hospital of Southern Medical University (SDHSMU; Guangzhou, China) into external testing cohort 2.
Who may be able to join
- Histologically confirmed stage I-III invasive BC
- Age ≥ 18 years
- The patient having undergone surgery
- The existence of MRI scans
Who cannot join
- Lacked pathological results
- Had other, simultaneous malignancies
- Had MR imaging issues were excluded
About this study
Breast cancer poses a significant global health challenge, especially among women, with high rates of recurrence and distant spread despite early interventions. The timely identification of metastasis risk and accurate prediction of treatment strategies are critical for improving prognosis. However, the complex heterogeneity of breast tumors presents challenges in precise prognosis prediction. Therefore, the development of innovative methods for tumor segmentation and prognosis assessment is essential.
The research conducted is a multicenter study that enrolled 1,199 non-metastatic breast cancer patients from four independent centers. Our study leverages the advancements in artificial intelligence (AI) to address this challenge. This study is the first successful application of MRI-based multimodal prediction system to precisely identify the risk of postoperative recurrence in breast cancer patients.
- Conditions
- Breast Cancer
- Being tested
- MRI
- Keywords
- deep learning
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
MRIOther
| Study group | Role | What they receive |
|---|---|---|
| Training cohort | We randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts. Receives: Other: MRI | |
| Internal validation cohort | We randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts. Receives: Other: MRI | |
| External testing cohort 1 | 432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1. Receives: Other: MRI | |
| External testing cohort 2 | 198 from Dongguan Tungwah Hospital (DTH; Dongguan, China) and Shunde Hospital of Southern Medical University (SDHSMU; Guangzhou, China) into external testing cohort 2. Receives: Other: MRI |
What is being measured
The primary outcome is the main question the study is designed to answer.
DFS
Disease-free survival
Measured: The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2024
- Study start23 Mar 2011
- Primary completion21 Sep 2019
- Study completion6 Dec 2021
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 CancerStudies of the same condition, open and under way.
- More trials run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityThe sponsor's other registered studies.
- 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
- 1,199 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yunfang Yu, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06546072
- Sponsor's protocol ID
- YSEC-KY-KS-2019-054-001
- Status last verified
- Aug 2024
- First submitted
- 6 Jul 2024
- Last updated
- 9 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Yu Y, Ren W, Mao L, Ouyang W, Hu Q, Yao Q, Tan Y, He Z, Ban X, Hu H, Lin R, Wang Z, Chen Y, Wu Z, Chen K, Ouyang J, Li T, Zhang Z, Liu G, Chen X, Li Z, Duan X, Wang J, Yao H. MRI-based multimodal AI model enables prediction of recurrence risk and adjuvant therapy in breast cancer. Pharmacol Res. 2025 Jun;216:107765. doi: 10.1016/j.phrs.2025.107765. Epub 2025 May 7.
PubMed 40345352
Study at a glance
From the registry record, last updated by the sponsor on 9 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning With MRI-based Multimodal-data Fusion Enhanced Postoperative Risk Stratification of Breast Cancer
- ClinicalTrials.gov ID
- NCT06546072
- Acronym / sponsor ID
- YSEC-KY-KS-2019-054-001
- Conditions
- Breast Cancer
- Keywords
- deep learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,199 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 23 Mar 2011 (actual)
- Primary completion
- 21 Sep 2019 (actual)
- Study completion
- 6 Dec 2021 (actual)
- First posted
- 9 Aug 2024 (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.
Breast cancer poses a significant global health challenge, especially among women, with high rates of recurrence and distant spread despite early interventions. The timely identification of metastasis risk and accurate prediction of treatment strategies are critical for improving prognosis. However, the complex heterogeneity of breast tumors presents challenges in precise prognosis prediction. Therefore, the development of innovative methods for tumor segmentation and prognosis assessment is essential.
The research conducted is a multicenter study that enrolled 1,199 non-metastatic breast cancer patients from four independent centers. Our study leverages the advancements in artificial intelligence (AI) to address this challenge. This study is the first successful application of MRI-based multimodal prediction system to precisely identify the risk of postoperative recurrence in breast cancer patients.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| Training cohort | – | Other: MRIWe randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts. |
| Internal validation cohort | – | Other: MRIWe randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts. |
| External testing cohort 1 | – | Other: MRI432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1. |
| External testing cohort 2 | – | Other: MRI198 from Dongguan Tungwah Hospital (DTH; Dongguan, China) and Shunde Hospital of Southern Medical University (SDHSMU; Guangzhou, China) into external testing cohort 2. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- In the study's initial phase, 1199 patients were randomly allocated at a ratio of 8:2 to training and testing datasets for automatic tumor segmentation. Subsequently, we randomly assigned 569 patients from Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH; Guangzhou, China) at a ratio of 3:1 to training (n = 456) and internal-validation (n = 113) cohorts for DFS prediction. The remaining patients were divided into two independent external-validation cohorts: 432 from Sun Yat-sen University Cancer Center (SYSUCC; Guangzhou, China) into external testing cohort 1, and 198 from Dongguan Tungwah Hospital (DTH; Dongguan, China) and Shunde Hospital of Southern Medical University (SDHSMU; Guangzhou, China) into external testing cohort 2.
Key inclusion criteria (4 listed)
- Histologically confirmed stage I-III invasive BC
- Age ≥ 18 years
- The patient having undergone surgery
- The existence of MRI scans
Key exclusion criteria (3 listed)
- Lacked pathological results
- Had other, simultaneous malignancies
- Had MR imaging issues were excluded
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 |
|---|---|---|
| MRI | Other | External testing cohort 1, External testing cohort 2, Internal validation cohort, Training cohort |
Primary outcome
DFS
Time frame: The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.
Disease-free survival
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Aug 2024.
- Derived from this study: PubMed 40345352Yu Y, Ren W, Mao L, Ouyang W, Hu Q, Yao Q, Tan Y, He Z, Ban X, Hu H, Lin R, Wang Z, Chen Y, Wu Z, Chen K, Ouyang J, Li T, Zhang Z, Liu G, Chen X, Li Z, Duan X, Wang J, Yao H. MRI-based multimodal AI model enables prediction of recurrence risk and adjuvant therapy in breast cancer. Pharmacol Res. 2025 Jun;216:107765. doi: 10.1016/j.phrs.2025.107765. Epub 2025 May 7.
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
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 4 inclusion and 3 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: finished, as of the sponsor's last update on 9 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
DFS (time frame: The time from surgery to tumor recurrence, including local and/or distant recurrence, disease progression, or death, assessed up to 100 months.). 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 NCT06546072, 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 9 Aug 2024.