Breast CancerARGUS projectNCT07244094
A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
An observational study of Breast Cancer, run by Second Affiliated Hospital, Zhejiang University, School of Medicine at 4 sites in China.
Trial summary
The goal of this observational study is to develop and validate an artificial intelligence (AI) model for predicting the risk of distant metastasis in patients with primary breast cancer. Read more about this study
- Who is it for?
- Ages 18 to 95 years, women only
- Conditions the study is for
- Breast Cancer
- What is being tested?
- Diagnostic Test: AI-Based Spatial Pathomic Analysis
- How many people
- 400 participants
- Where
- 4 sitesChina
- Study dates
- 15 Nov 2025to 7 Mar 2027
- 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 to 95 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: The study participants will be selected from a case-control cohort of adult female patients diagnosed with primary invasive breast cancer who underwent curative surgery at participating centers (e.g., The Second Affiliated Hospital of Zhejiang University) between January 2015 and December 2025. Eligible individuals must have available, high-quality archived primary tumor tissue samples, specifically H\&E-stained whole-slide images and consecutive sections for multiplex immunohistochemistry, coupled with complete clinicopathological data and long-term follow-up information documenting distant metastasis status. The final study sample will consist of patients from this source population who meet all predefined inclusion and exclusion criteria, ensuring data integrity and cohort homogeneity for AI model development.
Who may be able to join
- Female patients aged 18 years or older.
- Histologically confirmed primary invasive breast carcinoma.
- Underwent curative surgical resection (mastectomy or breast-conserving surgery) between January 2015 and December 2025.
- Before initiating the neoadjuvant therapy, there was a retention of the primary tumor specimen.
- Availability of high-quality, digitizable Hematoxylin and Eosin (H\&E) stained whole-slide images (WSIs).
- Availability of consecutive tissue sections from the same tumor block for multiplex immunohistochemistry (mIHC) staining (including markers such as Pan-CK, CD3, CD20).
- Complete clinicopathological data and follow-up information must be available, including but not limited to: TNM stage, histological grade, molecular subtype (ER, PR, HER2 status), adjuvant treatment records, and clearly documented distant metastasis-free survival (DMFS) data.
- A minimum follow-up of 5 years for patients with detailed information for distant metastasis events.
Who cannot join
- Pure ductal carcinoma in situ (DCIS) without an invasive component.
- Special histological subtypes of invasive carcinoma (e.g., metaplastic carcinoma) with distinct biological behaviors.
- No original lesion samples were retained before neoadjuvant therapy.
- Presence of contralateral breast cancer or a history of any other prior malignancy (except for cured non-melanoma skin cancer or carcinoma in situ of the cervix).
- H\&E or IHC slides with significant technical artifacts (e.g., fading, folds, heavy knife marks, tissue tearing, uneven staining) that preclude reliable image analysis.
- Low tumor cellularity (e.g., tumor area < 10% in the scanned field of view).
- Unavailable or unalignable consecutive tissue sections, preventing spatial registration of H\&E and mIHC images.
- Lack of essential clinicopathological or follow-up data required for model training or validation.
Trial locations and contacts
4 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China4 sites, 4 recruiting
- Not yet recruiting
Jilin Cancer Hospital
Changchun, Jilin, 130000
Tao Liu, 0431-85871915, 641159308@qq.com
- Recruiting
2nd Affiliated Hospital, School of Medicine, Zhejiang University, China
Hangzhou, Zhejiang
Jiaojiao Zhou, 0571-87784527, zhoujj@zju.edu.cn
- Not yet recruiting
The Fourth Affiliated Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang
Baizhou Li, 0579 89935398, Libaizhou@zju.edu.cn
- Not yet recruiting
Cancer Institute and Hospital, Tianjin Medical University, China
Tianjin, Tianjin Municipality, 300060
Xiaojing Guo, 022-23537796, guoxiaojing@tjmuch.com
About this study
The goal of this observational study is to develop and validate an artificial intelligence (AI) model for predicting the risk of distant metastasis in patients with primary breast cancer. The main question it aims to answer is:
Can a multimodal AI model, trained on routinely available histopathological images, accurately predict the long-term risk of breast cancer metastasis?
Researchers will analyze existing hematoxylin and eosin (H\&E) and immunohistochemistry (IHC) stained tissue slides from patients who underwent surgery between 2015 and 2025. Clinical data will be used to train the AI model and evaluate its performance in predicting metastasis.
- Conditions
- Breast Cancer
- Being tested
- Diagnostic Test: AI-Based Spatial Pathomic Analysis
- Keywords
- Breast Cancer, Artificial Intelligence, Distant Metastasis, Prediction
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Diagnostic Test: AI-Based Spatial Pathomic AnalysisOther
This is an observational study with no therapeutic or procedural interventions. The "intervention" refers to the analytical method applied to existing data. Archived tissue samples (H\&E and IHC stained slides) will be digitally scanned and analyzed by a multimodal artificial intelligence (AI) model to develop a risk prediction tool for distant metastasis. Patients' clinical data will be collected for model training and validation. No direct interaction with patients occurs, and no treatment decisions are influenced by this study.
| Study group | Role | What they receive |
|---|---|---|
| Patients with primary breast cancer who have experienced distant metastasis outcomes within 5 years | Not described Receives: Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis | |
| Patients with primary breast cancer who have not experienced distant metastasis for at least 5 years | Not described Receives: Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
Predictive accuracy for distant metastasis risk assessed by Time-dependent Area Under the Receiver Operating Characteristic Curve (Time-dependent AUC)
The Area Under the Receiver Operating Characteristic Curve (AUC) will be used to evaluate the model's binary classification performance in discriminating between patients with and without distant metastasis at the 5-year post-operative time point. This metric reflects the model's classification accuracy at a specific time.
Measured: From the date of initial surgery up to 5 years post-operatively, with the occurrence of distant metastasis defined as the event of interest.
Secondary outcomes (3)
Sensitivity and Specificity
Sensitivity and Specificity will be calculated at the optimal cut-off point of the model's risk score to evaluate its binary classification performance. Sensitivity measures the model's ability to correctly identify patients who develop distant metastasis (true positive rate), while Specificity measures its ability to correctly identify patients who do not (true negative rate).
Measured: Assessed at the 5-year post-operative time point.
Concordance Index (C-index)
Harrell's Concordance Index (C-index) will be employed to assess the model's overall prognostic discrimination ability throughout the follow-up period. It evaluates the consistency of the model's risk scores in correctly ranking the time to distant metastasis-free survival among individual patients.
Measured: From the time of the initial surgical treatment until distant metastasis occurs or until the end of the follow-up (the longest duration can be up to 10 years).
Model calibration assessed by calibration curve
The agreement between the model-predicted probability of distant metastasis and the observed actual incidence will be visualized and assessed using a calibration curve.
Measured: From the time of the initial surgical treatment until distant metastasis occurs or until the end of the follow-up (the longest duration can be up to 10 years).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Nov 2025
- Study start15 Nov 2025
- Primary completion30 Dec 2026 (estimated)
- Study completion7 Mar 2027 (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 Second Affiliated Hospital, Zhejiang University, School of MedicineThe 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 400 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07244094
- Sponsor's protocol ID
- 2025-1104
- Acronym
- ARGUS project
- Status last verified
- Feb 2026
- First submitted
- 17 Nov 2025
- Last updated
- 17 Feb 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
- ClinicalTrials.gov ID
- NCT07244094
- Acronym / sponsor ID
- ARGUS project / 2025-1104
- Conditions
- Breast Cancer
- Keywords
- Breast Cancer, Artificial Intelligence, Distant Metastasis, Prediction
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 400 participants (estimated)
- Lead sponsor
- Second Affiliated Hospital, Zhejiang University, School of MedicineUniversity, hospital or other
- Study start
- 15 Nov 2025 (actual)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 7 Mar 2027 (estimated)
- First posted
- 24 Nov 2025 (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.
The goal of this observational study is to develop and validate an artificial intelligence (AI) model for predicting the risk of distant metastasis in patients with primary breast cancer. The main question it aims to answer is:
Can a multimodal AI model, trained on routinely available histopathological images, accurately predict the long-term risk of breast cancer metastasis?
Researchers will analyze existing hematoxylin and eosin (H\&E) and immunohistochemistry (IHC) stained tissue slides from patients who underwent surgery between 2015 and 2025. Clinical data will be used to train the AI model and evaluate its performance in predicting metastasis.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Patients with primary breast cancer who have experienced distant metastasis outcomes within 5 years | – | Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis |
| Patients with primary breast cancer who have not experienced distant metastasis for at least 5 years | – | Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis |
Who may qualify
- Ages
- 18 to 95 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study participants will be selected from a case-control cohort of adult female patients diagnosed with primary invasive breast cancer who underwent curative surgery at participating centers (e.g., The Second Affiliated Hospital of Zhejiang University) between January 2015 and December 2025. Eligible individuals must have available, high-quality archived primary tumor tissue samples, specifically H\&E-stained whole-slide images and consecutive sections for multiplex immunohistochemistry, coupled with complete clinicopathological data and long-term follow-up information documenting distant metastasis status. The final study sample will consist of patients from this source population who meet all predefined inclusion and exclusion criteria, ensuring data integrity and cohort homogeneity for AI model development.
Key inclusion criteria (8 listed)
- Female patients aged 18 years or older.
- Histologically confirmed primary invasive breast carcinoma.
- Underwent curative surgical resection (mastectomy or breast-conserving surgery) between January 2015 and December 2025.
- Before initiating the neoadjuvant therapy, there was a retention of the primary tumor specimen.
- Availability of high-quality, digitizable Hematoxylin and Eosin (H\&E) stained whole-slide images (WSIs).
Key exclusion criteria (8 listed)
- Pure ductal carcinoma in situ (DCIS) without an invasive component.
- Special histological subtypes of invasive carcinoma (e.g., metaplastic carcinoma) with distinct biological behaviors.
- No original lesion samples were retained before neoadjuvant therapy.
- Presence of contralateral breast cancer or a history of any other prior malignancy (except for cured non-melanoma skin cancer or carcinoma in situ of the cervix).
- H\&E or IHC slides with significant technical artifacts (e.g., fading, folds, heavy knife marks, tissue tearing, uneven staining) that preclude reliable image analysis.
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 |
|---|---|---|
| Diagnostic Test: AI-Based Spatial Pathomic AnalysisThis is an observational study with no therapeutic or procedural interventions. The "intervention" refers to the analytical method applied to existing data. Archived tissue samples (H\&E and IHC stained slides) will be digitally scanned and analyzed by a multimodal artificial intelligence (AI) model to develop a risk prediction tool for distant metastasis. Patients' clinical data will be collected for model training and validation. No direct interaction with patients occurs, and no treatment decisions are influenced by this study. | Other | Patients with primary breast cancer who have experienced distant metastasis outcomes within 5 years, Patients with primary breast cancer who have not experienced distant metastasis for at least 5 years |
Primary outcome
Predictive accuracy for distant metastasis risk assessed by Time-dependent Area Under the Receiver Operating Characteristic Curve (Time-dependent AUC)
Time frame: From the date of initial surgery up to 5 years post-operatively, with the occurrence of distant metastasis defined as the event of interest.
The Area Under the Receiver Operating Characteristic Curve (AUC) will be used to evaluate the model's binary classification performance in discriminating between patients with and without distant metastasis at the 5-year post-operative time point. This metric reflects the model's classification accuracy at a specific time.
3 secondary outcomes
Sensitivity and Specificity
Time frame: Assessed at the 5-year post-operative time point.
Concordance Index (C-index)
Time frame: From the time of the initial surgical treatment until distant metastasis occurs or until the end of the follow-up (the longest duration can be up to 10 years).
Model calibration assessed by calibration curve
Time frame: From the time of the initial surgical treatment until distant metastasis occurs or until the end of the follow-up (the longest duration can be up to 10 years).
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 17 Feb 2026.
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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.
4 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 Second Affiliated Hospital, Zhejiang University, School of Medicine at 4 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 8 inclusion and 8 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 17 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 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 Jiaojiao Zhou (0571-87784527, zhoujj@zju.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Predictive accuracy for distant metastasis risk assessed by Time-dependent Area Under the Receiver Operating Characteristic Curve (Time-dependent AUC) (time frame: From the date of initial surgery up to 5 years post-operatively, with the occurrence of distant metastasis defined as the event of interest.). 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 17 Feb 2026.