Breast CancerNCT07689929
Research on AI Models for Predicting Breast Cancer Treatment Effectiveness to Guide Her-2 Targeted ADC Therapy
An observational study of Breast Cancer, run by Zhejiang Cancer Hospital at 1 site in China.
Trial summary
This study aims to develop an AI-based predictive tool to help clinicians more accurately determine whether breast cancer patients can benefit from HER-2-targeted antibody-drug conjugate (T-DXd) therapy before treatment. Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Breast Cancer
- How many people
- 900 participants
- Where
- 1 siteChina
- Study dates
- Aug 2026to Dec 2028
- 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 Any age
- Women only
- Child, Adult, Older adult
Study population: Retrospective cohort (modeling and internal validation): We collected data from breast cancer patients at Zhejiang Cancer Hospital who received Youherde treatment from January 2023 to June 2026, to build a dataset for developing an efficacy prediction model. We integrated the following multimodal information: 1. HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the most recent biopsy of recurrent or metastatic lesions before starting Youherde treatment; 2. For patients with post-surgery recurrence/metastasis and available surgical specimens, HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the primary tumor; 3. Corresponding clinical efficacy data (PFS, ORR, etc.) and proteomics data. Prospective cohort (multicenter external validation): Working together with multiple centers, we will prospectively include breast cancer patients who are planned to receive second-line or higher treatm
Who may be able to join
- Retrospective Cohort (Modeling and Validation):
- Female, 18 years or older;
- Advanced breast cancer confirmed by pathology (AJCC 8th edition, stage IV);
- HER2 status known;
- Received at least 2 cycles of Pyrotinib monotherapy;
- Complete baseline IHC slides (HER2, ER, PR, Ki-67) and HE-stained slides;
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- Efficacy assessed according to RECIST 1.1, with follow-up data (PFS or ORR).
- Prospective Cohort (External Validation):
- Meet criteria 1-3 above;
- Planning to receive Pyrotinib monotherapy as second-line or later treatment; if HER2-positive, previously received neoadjuvant/adjuvant H(P) therapy, and had metastatic recurrence within 12 months after completing treatment, with post-recurrence anti-HER2 therapy considered second-line treatment.
- Signed informed consent, agreeing to provide clinical info like imaging and pathology data before and after treatment.
Who cannot join
- All cohorts:
- Baseline IHC or HE slides of poor quality (e.g., faded, folded, or tissue loss >10%);
- Previous treatment with other HER2-ADC drugs;
- History of other malignancies (except non-melanoma skin cancer or cases with no recurrence for over 5 years);
- Participation in other interventional clinical trials at the same time (past trials already completed are fine);
- Lost to follow-up or missing key clinical data during treatment (e.g., efficacy evaluation, dose adjustment records);
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- Special treatment backgrounds that the AI model cannot analyze (e.g., combined local radiotherapy, severe infections, or other confounding factors).
- Additional exclusions for the prospective cohort:
- Pregnant or breastfeeding women;
- Contraindications to Üher (e.g., history of ILD, left ventricular ejection fraction <50%, etc.);
- Unable to comply with regular follow-up (e.g., living in a remote area, mental disorders).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: A Multi-center Retrospective Observational Study Using AI to Integrate Proteomics, Pathology, and Clinical Big Data to Build a Multi-modal Model for Predicting the Effectiveness of HER-2 Targeted ADC Therapy (T-DXd) in HER-2 Positive and Low-expression Advanced Breast Cancer, With External Validation.
This study aims to develop an AI-based predictive tool to help clinicians more accurately determine whether breast cancer patients can benefit from HER-2-targeted antibody-drug conjugate (T-DXd) therapy before treatment. While HER-2-targeted ADC drugs have significantly improved outcomes for patients with HER-2 positive and low-expression advanced breast cancer, there are notable individual differences in efficacy. Currently, there is a lack of precise clinical methods to predict response, which means some patients might receive ineffective treatment and face unnecessary drug side effects and financial burden.
This study is a retrospective multicenter observational study, planning to collect pathological images (including HE staining and HER-2, ER, PR, Ki-67 immunohistochemical staining), proteomics data, and clinical efficacy information from HER-2 positive and low-expression advanced breast cancer patients who have received T-DXd treatment.
Read more
The research will be carried out in five phases:
- Build a clinical database for ADC drug therapy, integrating basic patient information, treatment plans, efficacy data, and pathology specimen information from multiple centers.
- Use LC-MS/MS proteomics technology to screen for key protein markers related to T-DXd efficacy and use bioinformatics analysis to identify predictive protein indicators.
- Extract IHC staining features from pathological images and evaluate their correlation with efficacy alongside clinical data.
- Integrate proteomics, pathology, and clinical big data, using AI technologies such as foundational pathology models (like TITAN), biomedical large language models (like BioBERT), and protein large language models (like ESM2-15B). Apply a multiple instance learning strategy to build a multimodal efficacy prediction model, and evaluate the model's performance on the training set using 5-fold cross-validation.
- Establish an internal validation cohort (200 cases) and a multicenter external validation cohort (300 cases). Considering that the external validation group may lack proteomics data, the multimodal model will be fine-tuned and distilled into a simplified predictive model based on standard IHC features (HER-2, ER, PR, Ki-67, plus key protein markers identified from proteomics) and clinical text information, then its performance will be verified in the external cohort.
Ultimately, this research will create an AI tool to support clinical decision-making, promoting personalized treatment for HER-2 positive and low-expression breast cancer and the clinical adoption of AI in healthcare.
Read the detailed description
Ambispective study combining retrospective model development and prospective validation.
- Conditions
- Breast Cancer
- 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 |
|---|---|---|
| HER2-positive T-DXd-sensitive cohort | Not described | |
| HER2-positive T-DXd-resistant cohort | Not described | |
| HER2-low T-DXd-sensitive cohort | Not described | |
| HER2-low T-DXd-resistant cohort | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve (AUC) of the Predictive Model
Measured: Baseline (at initial diagnosis)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2026
- Study startAug 2026 (estimated)
- Primary completionJun 2028 (estimated)
- Study completionDec 2028 (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.
- 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
- Other
- Enrolment
- 900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Zhejiang Cancer Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Hu Hai, Zhejiang Cancer Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07689929
- Sponsor's protocol ID
- IRB-2026-60（ⅡT）
- Status last verified
- Jul 2026
- First submitted
- 1 Jul 2026
- Last updated
- 8 Jul 2026
- Results posted
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-center Retrospective Observational Study Using AI to Integrate Proteomics, Pathology, and Clinical Big Data to Build a Multi-modal Model for Predicting the Effectiveness of HER-2 Targeted ADC Therapy (T-DXd) in HER-2 Positive and Low-expression Advanced Breast Cancer, With External Validation.
- ClinicalTrials.gov ID
- NCT07689929
- Acronym / sponsor ID
- IRB-2026-60（ⅡT）
- Conditions
- Breast Cancer
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 900 participants (estimated)
- Lead sponsor
- Zhejiang Cancer HospitalUniversity, hospital or other
- Study start
- Aug 2026 (estimated)
- Primary completion
- Jun 2028 (estimated)
- Study completion
- Dec 2028 (estimated)
- First posted
- 8 Jul 2026 (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 study aims to develop an AI-based predictive tool to help clinicians more accurately determine whether breast cancer patients can benefit from HER-2-targeted antibody-drug conjugate (T-DXd) therapy before treatment. While HER-2-targeted ADC drugs have significantly improved outcomes for patients with HER-2 positive and low-expression advanced breast cancer, there are notable individual differences in efficacy. Currently, there is a lack of precise clinical methods to predict response, which means some patients might receive ineffective treatment and face unnecessary drug side effects and financial burden.
This study is a retrospective multicenter observational study, planning to collect pathological images (including HE staining and HER-2, ER, PR, Ki-67 immunohistochemical staining), proteomics data, and clinical efficacy information from HER-2 positive and low-expression advanced breast cancer patients who have received T-DXd treatment.
The research will be carried out in five phases:
- Build a clinical database for ADC drug therapy, integrating basic patient information, treatment plans, efficacy data, and pathology specimen information from multiple centers.
- Use LC-MS/MS proteomics technology to screen for key protein markers related to T-DXd efficacy and use bioinformatics analysis to identify predictive protein indicators.
- Extract IHC staining features from pathological images and evaluate their correlation with efficacy alongside clinical data.
- Integrate proteomics, pathology, and clinical big data, using AI technologies such as foundational pathology models (like TITAN), biomedical large language models (like BioBERT), and protein large language models (like ESM2-15B). Apply a multiple instance learning strategy to build a multimodal efficacy prediction model, and evaluate the model's performance on the training set using 5-fold cross-validation.
- Establish an internal validation cohort (200 cases) and a multicenter external validation cohort (300 cases). Considering that the external validation group may lack proteomics data, the multimodal model will be fine-tuned and distilled into a simplified predictive model based on standard IHC features (HER-2, ER, PR, Ki-67, plus key protein markers identified from proteomics) and clinical text information, then its performance will be verified in the external cohort.
Ultimately, this research will create an AI tool to support clinical decision-making, promoting personalized treatment for HER-2 positive and low-expression breast cancer and the clinical adoption of AI in healthcare.
Detailed description
Ambispective study combining retrospective model development and prospective validation.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 4
| Arm | Type | Receives |
|---|---|---|
| HER2-positive T-DXd-sensitive cohort | – | – |
| HER2-positive T-DXd-resistant cohort | – | – |
| HER2-low T-DXd-sensitive cohort | – | – |
| HER2-low T-DXd-resistant cohort | – | – |
Who may qualify
- Sex
- Female only
- Age groups
- Child, Adult, Older adult
- Study population
- Retrospective cohort (modeling and internal validation): We collected data from breast cancer patients at Zhejiang Cancer Hospital who received Youherde treatment from January 2023 to June 2026, to build a dataset for developing an efficacy prediction model. We integrated the following multimodal information: 1. HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the most recent biopsy of recurrent or metastatic lesions before starting Youherde treatment; 2. For patients with post-surgery recurrence/metastasis and available surgical specimens, HE-stained slides, blank slides, and IHC-stained tissue slide images for ER, PR, HER-2, and Ki-67 from the primary tumor; 3. Corresponding clinical efficacy data (PFS, ORR, etc.) and proteomics data. Prospective cohort (multicenter external validation): Working together with multiple centers, we will prospectively include breast cancer patients who are planned to receive second-line or higher treatm
Key inclusion criteria (11 listed)
- Retrospective Cohort (Modeling and Validation):
- Prospective Cohort (External Validation):
Key exclusion criteria (11 listed)
- All cohorts:
- Additional exclusions for the prospective cohort:
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
Area Under the Receiver Operating Characteristic Curve (AUC) of the Predictive Model
Time frame: Baseline (at initial diagnosis)
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 8 Jul 2026.
- Individual participant dataSharing planned: No
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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.
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 Zhejiang Cancer Hospital 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 11 inclusion and 11 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: opening soon, as of the sponsor's last update on 8 Jul 2026. 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?
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 Hai Hu (13556111018, huhai@zjcc.org.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (AUC) of the Predictive Model (time frame: Baseline (at initial diagnosis)). 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 8 Jul 2026.