Breast CancerNCT06510127
An AI Model Predicts the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: a Multicenter, Bidirectional Cohort Study
An observational study of Breast Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial summary
Neoadjuvant chemotherapy is an important part of the systematic treatment of breast cancer, and it is of great clinical significance to predict the efficacy of neoadjuvant chemotherapy in early stage. The emergence of multi-modal artificial intelligence model has brought new ideas for it. Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 840 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2024to 31 Jan 2026
- 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 Any age
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: A total of 840 people will be enrolled in this study, including 300 patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 200 patients treated in the South Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 170 patients treated in external centers. Prospective collection of data from 170 patients treated at Sun Yat-sen Memorial Hospital.
Who may be able to join
- Women
- Pathological diagnosis of non-metastatic invasive breast cancer (stage II-III)
- At least 4 cycles of neoadjuvant chemotherapy
- Radical surgery was performed after neoadjuvant chemotherapy
- There are pathological images and reports of breast puncture specimens before neoadjuvant chemotherapy
- 'There are MRI images and reports of breast MRI within 2 weeks before neoadjuvant chemotherapy
- There are standard clinical records
Who cannot join
- Inflammatory breast cancer
- Bilateral breast cancer
- Newly diagnosed stage IV breast cancer
- Other tumors have not been completely removed or less than 3 years after surgery
- Treatment other than neoadjuvant therapy had been performed before surgery
Trial locations and contacts
1 site in 1 country.
China1 site
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Guangzhou, Guangdong
About this study
Official title: An AI Large Language Model Based on Multi-task and Multimodal Data Fusion Accurately Predicts the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: a Multicenter, Bidirectional Cohort Study
Neoadjuvant chemotherapy is an important part of the systematic treatment of breast cancer, and it is of great clinical significance to predict the efficacy of neoadjuvant chemotherapy in early stage. The emergence of multi-modal artificial intelligence model has brought new ideas for it. However, the limited ability of artificial intelligence to integrate multi-modal data, the lack of multi-modal models, and the insufficient level of evidence in clinical promotion of artificial intelligence are all scientific problems that need to be solved. In the early stage of the study, a variety of artificial intelligence accurate prediction and auxiliary diagnosis and treatment models for breast cancer were constructed based on magnetic resonance imaging and pathomics, etc., and the effectiveness of the models in predicting the curative effect of neoadjuvant chemotherapy for breast cancer was explored. In order to further improve the predictive efficiency of the model and fill the gap in the systematic study of multi-modal data fusion model, this clinical study intends to combine pathological images, magnetic resonance imaging, diagnostic report text and clinical variables to establish an artificial intelligence large language model based on multi-task and multi-modal data fusion to accurately predict the efficacy of neoadjuvant chemotherapy for breast cancer. A multicenter, bidirectional cohort study was conducted to explore the predictive effectiveness of the model.
Read the detailed description
This is a multicenter, bidirectional cohort study. Retrospective training cohort, retrospective validation cohort and prospective test cohort were designed.
Data of patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for training cohort, and data of patients treated in the South ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University for internal validation cohort; Data on patients treated at external centers between January 1, 2002 and August 31, 2023 were retrospectively collected for external validation cohort. Data on patients admitted to Sun Yat-sen Memorial Hospital at Sun Yat-sen University after January 1, 2024 were prospectively collected for the test cohort. Patient data collected included: pathological images and report texts of breast puncture specimens before neoadjuvant chemotherapy, breast magnetic resonance images and report texts before neoadjuvant chemotherapy, postoperative pathological reports and clinical information, etc.. An artificial intelligence large language model based on multi-task and multi-modal data integration was established to accurately predict the efficacy of neoadjuvant chemotherapy for breast cancer, and its predictive efficacy was tested by retrospective validation cohort and prospective double-blind test cohort. The retrospective cohort of this study was followed up to collect clinical data, magnetic resonance imaging and reports, and surgical pathology reports of patients, etc.. When patients had disease recurrence, the DFS time of patients was recorded, and when patients did not have disease recurrence, the last follow-up time was recorded. Baseline data survey was completed during hospitalization of prospective cohort patients. Pathological reports of breast tumors surgically removed after neoadjuvant chemotherapy were obtained during follow-up, as well as the time of disease recurrence and the time of death of patients. Clinical information such as magnetic resonance imaging and reports were collected during follow-up. Follow-up until the end of the 2-year study.
- Conditions
- Breast Cancer
- Keywords
- breast cancer, neoadjuvant chemotherapy, artificial intelligence
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| training cohort | Data of patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for the training cohort | |
| internal validation cohort | Data of patients treated in the South Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for the internal validation cohort | |
| external validation cohort | Data on patients treated at external centers between January 1, 2002 and August 31, 2023 were retrospectively collected for the external validation cohort | |
| test cohort | Data on patients admitted to Sun Yat-sen Memorial Hospital of Sun Yat-sen University after January 1, 2024 were prospectively collected for the test cohort |
What is being measured
The primary outcome is the main question the study is designed to answer.
Predictive ability of the model for pCR after neoadjuvant chemotherapy in breast cancer patients
receiver operating characteristic curve (ROC curve) were used to evaluate the predictive efficiency of the model
Measured: 1 year
Secondary outcomes (1)
Predictive ability of the model for DFS after neoadjuvant chemotherapy in breast cancer patients
receiver operating characteristic curve (ROC curve) were used to evaluate the predictive efficiency of the model
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Jul 2024
- Study start1 Aug 2024 (estimated)
- Primary completion1 Jun 2025 (estimated)
- Study completion31 Jan 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- 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
- Cross-sectional
- Enrolment
- 840 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- First Affiliated Hospital of Jinan University
- Responsible party
- Principal investigator, Yunfang Yu, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Investigators
- Yunfang Yu, Doctor, Study chair, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityHerui Yao, Doctor, Study director, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityKai Chen, Doctor, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityYan Nie, Doctor, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityXiaohui Duan, Doctor, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityJingjing Han, Master, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityYanchun Li, Bachelor, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityWei Ren, Doctor, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06510127
- Sponsor's protocol ID
- SYSKY-2024-276-01
- Status last verified
- Jul 2024
- First submitted
- 4 Jul 2024
- Last updated
- 19 Jul 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An AI Large Language Model Based on Multi-task and Multimodal Data Fusion Accurately Predicts the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: a Multicenter, Bidirectional Cohort Study
- ClinicalTrials.gov ID
- NCT06510127
- Acronym / sponsor ID
- SYSKY-2024-276-01
- Conditions
- Breast Cancer
- Keywords
- breast cancer, neoadjuvant chemotherapy, artificial intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 840 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- First Affiliated Hospital of Jinan University
- Study start
- 1 Aug 2024 (estimated)
- Primary completion
- 1 Jun 2025 (estimated)
- Study completion
- 31 Jan 2026 (estimated)
- First posted
- 19 Jul 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.
Neoadjuvant chemotherapy is an important part of the systematic treatment of breast cancer, and it is of great clinical significance to predict the efficacy of neoadjuvant chemotherapy in early stage. The emergence of multi-modal artificial intelligence model has brought new ideas for it. However, the limited ability of artificial intelligence to integrate multi-modal data, the lack of multi-modal models, and the insufficient level of evidence in clinical promotion of artificial intelligence are all scientific problems that need to be solved. In the early stage of the study, a variety of artificial intelligence accurate prediction and auxiliary diagnosis and treatment models for breast cancer were constructed based on magnetic resonance imaging and pathomics, etc., and the effectiveness of the models in predicting the curative effect of neoadjuvant chemotherapy for breast cancer was explored. In order to further improve the predictive efficiency of the model and fill the gap in the systematic study of multi-modal data fusion model, this clinical study intends to combine pathological images, magnetic resonance imaging, diagnostic report text and clinical variables to establish an artificial intelligence large language model based on multi-task and multi-modal data fusion to accurately predict the efficacy of neoadjuvant chemotherapy for breast cancer. A multicenter, bidirectional cohort study was conducted to explore the predictive effectiveness of the model.
Detailed description
This is a multicenter, bidirectional cohort study. Retrospective training cohort, retrospective validation cohort and prospective test cohort were designed.
Data of patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for training cohort, and data of patients treated in the South ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University for internal validation cohort; Data on patients treated at external centers between January 1, 2002 and August 31, 2023 were retrospectively collected for external validation cohort. Data on patients admitted to Sun Yat-sen Memorial Hospital at Sun Yat-sen University after January 1, 2024 were prospectively collected for the test cohort. Patient data collected included: pathological images and report texts of breast puncture specimens before neoadjuvant chemotherapy, breast magnetic resonance images and report texts before neoadjuvant chemotherapy, postoperative pathological reports and clinical information, etc.. An artificial intelligence large language model based on multi-task and multi-modal data integration was established to accurately predict the efficacy of neoadjuvant chemotherapy for breast cancer, and its predictive efficacy was tested by retrospective validation cohort and prospective double-blind test cohort. The retrospective cohort of this study was followed up to collect clinical data, magnetic resonance imaging and reports, and surgical pathology reports of patients, etc.. When patients had disease recurrence, the DFS time of patients was recorded, and when patients did not have disease recurrence, the last follow-up time was recorded. Baseline data survey was completed during hospitalization of prospective cohort patients. Pathological reports of breast tumors surgically removed after neoadjuvant chemotherapy were obtained during follow-up, as well as the time of disease recurrence and the time of death of patients. Clinical information such as magnetic resonance imaging and reports were collected during follow-up. Follow-up until the end of the 2-year study.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| training cohort | – | –Data of patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for the training cohort |
| internal validation cohort | – | –Data of patients treated in the South Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 1, 2002 to August 31, 2023 were retrospectively collected for the internal validation cohort |
| external validation cohort | – | –Data on patients treated at external centers between January 1, 2002 and August 31, 2023 were retrospectively collected for the external validation cohort |
| test cohort | – | –Data on patients admitted to Sun Yat-sen Memorial Hospital of Sun Yat-sen University after January 1, 2024 were prospectively collected for the test cohort |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- A total of 840 people will be enrolled in this study, including 300 patients treated in the North Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 200 patients treated in the South Ward of Sun Yat-sen Memorial Hospital of Sun Yat-sen University, 170 patients treated in external centers. Prospective collection of data from 170 patients treated at Sun Yat-sen Memorial Hospital.
Key inclusion criteria (7 listed)
- Women
- Pathological diagnosis of non-metastatic invasive breast cancer (stage II-III)
- At least 4 cycles of neoadjuvant chemotherapy
- Radical surgery was performed after neoadjuvant chemotherapy
- There are pathological images and reports of breast puncture specimens before neoadjuvant chemotherapy
Key exclusion criteria (5 listed)
- Inflammatory breast cancer
- Bilateral breast cancer
- Newly diagnosed stage IV breast cancer
- Other tumors have not been completely removed or less than 3 years after surgery
- Treatment other than neoadjuvant therapy had been performed before surgery
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Predictive ability of the model for pCR after neoadjuvant chemotherapy in breast cancer patients
Time frame: 1 year
receiver operating characteristic curve (ROC curve) were used to evaluate the predictive efficiency of the model
1 secondary outcome
Predictive ability of the model for DFS after neoadjuvant chemotherapy in breast cancer patients
Time frame: 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Jul 2024.
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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.
Yunfang Yu, Doctor study chair
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Herui Yao, Doctor study director
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Kai Chen, Doctor principal investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
1 site in 1 country: see every site and its status.
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 at 1 site 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 7 inclusion and 5 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 19 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site 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?
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?
Predictive ability of the model for pCR after neoadjuvant chemotherapy in breast cancer patients (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 19 Jul 2024; AzTrials last refreshed its copy on 7 Nov 2026.