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

NCT07244094 | Recruiting DMC

• 1 other identifier: 2025-1104
• Study Type: observational
• Target: 400
• Locations: 1 country
• Sites: 4

Brief 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. 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

Keywords

Breast Cancer; Artificial Intelligence; Distant Metastasis; Prediction

Outcome Measures

Primary Outcomes (1)

• 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.

  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

  Assessed at the 5-year post-operative time point.

• Concordance Index (C-index)

  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

  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).

Study Arms (2)

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

Interventions

Diagnostic Test: AI-Based Spatial Pathomic Analysis OTHER

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 experienced distant metastasis outcomes within 5 years; Patients with primary breast cancer who have not experienced distant metastasis for at least 5 years

Eligibility Criteria

• Age: 18 Years - 95 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Second Affiliated Hospital, School of Medicine, Zhejiang University: lead

Study Sites (4)

Jilin Cancer Hospital

Changchun, Jilin, 130000, China

NOT YET RECRUITING

Cancer Institute and Hospital, Tianjin Medical University, China

Tianjin, Tianjin Municipality, 300060, China

NOT YET RECRUITING

2nd Affiliated Hospital, School of Medicine, Zhejiang University, China

Hangzhou, Zhejiang, China

RECRUITING

The Fourth Affiliated Hospital of Zhejiang University School of Medicine

Hangzhou, Zhejiang, China

NOT YET RECRUITING

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Central Study Contacts

Jiaojiao Zhou

CONTACT

0571-87784527; zhoujj@zju.edu.cn

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 17, 2025
• First Posted: November 24, 2025
• Study Start: November 15, 2025
• Primary Completion (Estimated): December 30, 2026
• Study Completion (Estimated): March 7, 2027
• Last Updated: February 17, 2026

Record last verified: 2026-02

Locations

CN (4)