A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
NCT ID: NCT07244094
Last Updated: 2025-11-24
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
400 participants
OBSERVATIONAL
2025-11-15
2027-03-07
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
RETROSPECTIVE
Study Groups
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Patients with primary breast cancer who have experienced distant metastasis outcomes within 5 years
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
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.
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
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
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.
18 Years
95 Years
FEMALE
No
Sponsors
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Second Affiliated Hospital, School of Medicine, Zhejiang University
OTHER
Responsible Party
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Locations
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Jilin Cancer Hospital
Changchun, Jilin, China
Cancer Institute and Hospital, Tianjin Medical University, China
Tianjin, Tianjin Municipality, China
2nd Affiliated Hospital, School of Medicine, Zhejiang University, China
Hangzhou, Zhejiang, China
The Fourth Affiliated Hospital of Zhejiang University School of Medicine
Hangzhou, Zhejiang, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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2025-1104
Identifier Type: -
Identifier Source: org_study_id
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