A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps (ARGUS project)

February 11, 2026 updated by: Second Affiliated Hospital, School of Medicine, Zhejiang University

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 Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

400

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Jilin
      • Changchun, Jilin, China, 130000
        • Not yet recruiting
        • Jilin Cancer Hospital
        • Contact:
    • Tianjin Municipality
      • Tianjin, Tianjin Municipality, China, 300060
        • Not yet recruiting
        • Cancer Institute and Hospital, Tianjin Medical University, China
        • Contact:
    • Zhejiang
      • Hangzhou, Zhejiang, China
        • Recruiting
        • 2nd Affiliated Hospital, School of Medicine, Zhejiang University, China
        • Contact:
      • Hangzhou, Zhejiang, China
        • Not yet recruiting
        • The Fourth Affiliated Hospital of Zhejiang University School of Medicine
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

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.

Description

Inclusion Criteria:

  1. Female patients aged 18 years or older.
  2. Histologically confirmed primary invasive breast carcinoma.
  3. Underwent curative surgical resection (mastectomy or breast-conserving surgery) between January 2015 and December 2025.
  4. Before initiating the neoadjuvant therapy, there was a retention of the primary tumor specimen.
  5. Availability of high-quality, digitizable Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs).
  6. Availability of consecutive tissue sections from the same tumor block for multiplex immunohistochemistry (mIHC) staining (including markers such as Pan-CK, CD3, CD20).
  7. 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.
  8. A minimum follow-up of 5 years for patients with detailed information for distant metastasis events.

Exclusion Criteria:

  1. Pure ductal carcinoma in situ (DCIS) without an invasive component.
  2. Special histological subtypes of invasive carcinoma (e.g., metaplastic carcinoma) with distinct biological behaviors.
  3. No original lesion samples were retained before neoadjuvant therapy.
  4. 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).
  5. 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.
  6. Low tumor cellularity (e.g., tumor area < 10% in the scanned field of view).
  7. Unavailable or unalignable consecutive tissue sections, preventing spatial registration of H&E and mIHC images.
  8. Lack of essential clinicopathological or follow-up data required for model training or validation.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients with primary breast cancer who have experienced distant metastasis outcomes within 5 years
Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis
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.
Patients with primary breast cancer who have not experienced distant metastasis for at least 5 years
Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
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.
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity and Specificity
Time Frame: Assessed at the 5-year post-operative time point.
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).
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).
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.
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).
The agreement between the model-predicted probability of distant metastasis and the observed actual incidence will be visualized and assessed using a calibration curve.
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).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Second Affiliated Hospital, School of Medicine, Zhejiang University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 15, 2025

Primary Completion (Estimated)

December 30, 2026

Study Completion (Estimated)

March 7, 2027

Study Registration Dates

First Submitted

November 17, 2025

First Submitted That Met QC Criteria

November 17, 2025

First Posted (Actual)

November 24, 2025

Study Record Updates

Last Update Posted (Actual)

February 17, 2026

Last Update Submitted That Met QC Criteria

February 11, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2025-1104

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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