Breast CancerNCT06649565
Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
An observational study of Breast Cancer, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences at 2 sites in China.
Trial summary
Breast cancer has become the world's number one cancer. While its therapeutic efficacy is increasing, how to achieve non-invasive evaluation of the efficacy of neoadjuvant therapy (NAT) for breast cancer patients and thus avoid surgery has become a bottleneck problem that needs to be broken through in clinical… Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 300 participants
- Where
- 2 sitesChina
- Study dates
- 1 Jan 2024to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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: This study adopts sequential enrollment (continuously recruiting patients until the sample size requirement is met). The subjects are patients with neoadjuvant treatment for breast cancer who were treated in Beijing Second Hospital, Beijing Luhe Hospital Affiliated to Capital Medical University, and Beijing Sanhuan Cancer Hospital from January 2024 to December 2026. Each independent medical center will include 100 patients, for a total of 300 patients.
Who may be able to join
- Patients who were treated in the above research centers between January 1, 2024 and October 31, 2025;
- ≥18 years old, female, ECOG score ≤2;
- Pathological biopsy confirmed invasive breast cancer;
- AJCC (8th edition) stage I-III;
- MRI imaging data before and after neoadjuvant therapy;
- Planned mastectomy or breast-conserving surgery after neoadjuvant therapy, and postoperative pathological information obtained.
Who cannot join
- Bilateral breast cancer, multiple lesions, or occult breast cancer;
- Poor MRI data quality;
- Patients who had received other anti-tumor treatments before enrollment;
- Patients with other malignant tumors
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
Sanhuan Cancer Hospital, Chaoyang District, Beijing(Cancer Hospital, Chinese Academy of Medical Sciences, close medical alliance)
Beijing, Beijing Municipality, 100000
zhang yu rong, 010-67487559, shzlyyllwyh@163.com
- Recruiting
Cancer Hospital, Chinese Academy of Medical Sciences
Beijing, Beijing Municipality, 100021
Peng Yuan, 13811967690, yuanpengyp01@163.com
About this study
Breast cancer has become the world's number one cancer. While its therapeutic efficacy is increasing, how to achieve non-invasive evaluation of the efficacy of neoadjuvant therapy (NAT) for breast cancer patients and thus avoid surgery has become a bottleneck problem that needs to be broken through in clinical diagnosis and treatment. Existing non-invasive evaluation strategies are limited to single-center, single-modality modeling, and have problems such as low performance and poor versatility. Therefore, in the early stage of this study, multi-modality breast cancer patient data from multiple centers across the country were collected and the establishment of an artificial intelligence (AI) efficacy prediction model was preliminarily completed. On this basis, this project intends to further improve the multi-center prospective validation study of the prediction model. The research results will help solve the scientific problem of non-invasive judgment of NAT efficacy in breast cancer patients and provide a new paradigm for the research of high-performance AI diagnosis and treatment auxiliary systems applicable to multiple centers.
Read the detailed description
(1) Prospectively collect breast MRI original images (DCE and ADC sequences) and corresponding clinical and surgical pathological data of multi-center breast cancer patients before and after neoadjuvant treatment, store and transport them via mobile hard disks, and input the processed data into the established efficacy determination model stored in a dedicated cloud server; (2) Use artificial intelligence to automatically delineate the ROI area and extract the imaging genomics and deep learning features therein, and combine the clinical pathological characteristics of the patients to further prospectively verify the effectiveness of the established pCR efficacy determination model.
- Conditions
- Breast Cancer
- Being tested
- no intervention
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
no interventionOther
no intervention
| Study group | Role | What they receive |
|---|---|---|
| Breast cancer patients who achieved pathological complete response after neoadjuvant therapy | All enrolled breast cancer patients received normal neoadjuvant therapy and subsequent surgery without intervention in the diagnosis and treatment process. They were judged to have achieved pathological complete respone based on surgical pathology. Receives: Other: no intervention | |
| Breast cancer patients who did not achieve pathological complete response after neoadjuvant therapy | All enrolled breast cancer patients received normal neoadjuvant therapy and subsequent surgery without intervention in the diagnosis and treatment process. They were judged as not achieving pathological complete respone based on surgical pathology. Receives: Other: no intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Breast MRI radiomics characteristics of breast cancer patients during neoadjuvant therapy
Measured: Breast cancer MRI images before neoadjuvant therapy and immediately after completing neoadjuvant therapy
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Oct 2024
- Study start1 Jan 2024
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 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 Cancer Institute and Hospital, Chinese Academy of Medical SciencesThe 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
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, PENG YUAN, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
- Investigators
- peng yuan, doctor, Study director, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06649565
- Sponsor's protocol ID
- 2024-1- 4021
- Status last verified
- Aug 2024
- First submitted
- 24 Jun 2024
- Last updated
- 18 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Validation and Application of an Artificial Intelligence-based Model for Evaluating the Efficacy of Breast Cancer Patients After Neoadjuvant Therapy
- ClinicalTrials.gov ID
- NCT06649565
- Acronym / sponsor ID
- 2024-1- 4021
- Conditions
- Breast Cancer
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Cancer Institute and Hospital, Chinese Academy of Medical SciencesUniversity, hospital or other
- Study start
- 1 Jan 2024 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 18 Oct 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 has become the world's number one cancer. While its therapeutic efficacy is increasing, how to achieve non-invasive evaluation of the efficacy of neoadjuvant therapy (NAT) for breast cancer patients and thus avoid surgery has become a bottleneck problem that needs to be broken through in clinical diagnosis and treatment. Existing non-invasive evaluation strategies are limited to single-center, single-modality modeling, and have problems such as low performance and poor versatility. Therefore, in the early stage of this study, multi-modality breast cancer patient data from multiple centers across the country were collected and the establishment of an artificial intelligence (AI) efficacy prediction model was preliminarily completed. On this basis, this project intends to further improve the multi-center prospective validation study of the prediction model. The research results will help solve the scientific problem of non-invasive judgment of NAT efficacy in breast cancer patients and provide a new paradigm for the research of high-performance AI diagnosis and treatment auxiliary systems applicable to multiple centers.
Detailed description
(1) Prospectively collect breast MRI original images (DCE and ADC sequences) and corresponding clinical and surgical pathological data of multi-center breast cancer patients before and after neoadjuvant treatment, store and transport them via mobile hard disks, and input the processed data into the established efficacy determination model stored in a dedicated cloud server; (2) Use artificial intelligence to automatically delineate the ROI area and extract the imaging genomics and deep learning features therein, and combine the clinical pathological characteristics of the patients to further prospectively verify the effectiveness of the established pCR efficacy determination model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Breast cancer patients who achieved pathological complete response after neoadjuvant therapy | – | Other: no interventionAll enrolled breast cancer patients received normal neoadjuvant therapy and subsequent surgery without intervention in the diagnosis and treatment process. They were judged to have achieved pathological complete respone based on surgical pathology. |
| Breast cancer patients who did not achieve pathological complete response after neoadjuvant therapy | – | Other: no interventionAll enrolled breast cancer patients received normal neoadjuvant therapy and subsequent surgery without intervention in the diagnosis and treatment process. They were judged as not achieving pathological complete respone based on surgical pathology. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study adopts sequential enrollment (continuously recruiting patients until the sample size requirement is met). The subjects are patients with neoadjuvant treatment for breast cancer who were treated in Beijing Second Hospital, Beijing Luhe Hospital Affiliated to Capital Medical University, and Beijing Sanhuan Cancer Hospital from January 2024 to December 2026. Each independent medical center will include 100 patients, for a total of 300 patients.
Key inclusion criteria (6 listed)
- Patients who were treated in the above research centers between January 1, 2024 and October 31, 2025;
- ≥18 years old, female, ECOG score ≤2;
- Pathological biopsy confirmed invasive breast cancer;
- AJCC (8th edition) stage I-III;
- MRI imaging data before and after neoadjuvant therapy;
Key exclusion criteria (4 listed)
- Bilateral breast cancer, multiple lesions, or occult breast cancer;
- Poor MRI data quality;
- Patients who had received other anti-tumor treatments before enrollment;
- Patients with other malignant tumors
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 interventionno intervention | Other | Breast cancer patients who achieved pathological complete response after neoadjuvant therapy, Breast cancer patients who did not achieve pathological complete response after neoadjuvant therapy |
Primary outcome
Breast MRI radiomics characteristics of breast cancer patients during neoadjuvant therapy
Time frame: Breast cancer MRI images before neoadjuvant therapy and immediately after completing neoadjuvant therapy
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Oct 2024.
- Individual participant dataSharing planned: No
Related trials for Breast Cancer
Active, not recruiting
Copper Cu 64-DOTA-Trastuzumab PET in Predicting Response to Treatment With Ado-Trastuzumab Emtansine in Patients With Metastatic HER2 Positive Breast Cancer
Bone Metastases, HER2-positive Breast Cancer
Active, not recruitingObservational study
A ReaL World Study of DS-8201
Breast Cancer Metastatic, Treatment Efficacy
RecruitingPhase 3
A Clinical Study of Patritumab Deruxtecan to Treat Breast Cancer (MK-1022-016)
Breast Neoplasms
RecruitingObservational study
Characterization of Extracellular Vesicles in Breast Cancer Patients
Breast Cancer
Not yet recruitingPhase 4
Evaluation of the Efficacy and Acceptability of a Phytotherapy Combining Devil's Claw, Blackcurrant and Meadowsweet for the Management of Aromatase Inhibitor-induced Arthralgia in Patients Treated for Localized Breast Cancer in the Adjuvant Setting
Hormone Therapy-induced Arthralgia, Phytotherapy
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.
peng yuan, doctor study director
Cancer Institute and Hospital, Chinese Academy of Medical Sciences
2 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 Cancer, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences at 2 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 6 inclusion and 4 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: looking for participants now, as of the sponsor's last update on 18 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact peng yuan, doctor (01087787242, yuanpengyp01@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Breast MRI radiomics characteristics of breast cancer patients during neoadjuvant therapy (time frame: Breast cancer MRI images before neoadjuvant therapy and immediately after completing neoadjuvant therapy). 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 18 Oct 2024.