Predicting breast cancer spread with AI-generated spatial pathology maps

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study will train and validate a multimodal AI model using archived H&E whole-slide images and consecutive multiplex IHC sections from surgically resected primary breast tumors. Clinical and pathologic data (TNM stage, grade, receptor status, treatments) and at least five years of follow-up including distant metastasis-free survival will be used to label and tune the model. The AI approach focuses on spatial pathomics features (e.g., Pan-CK, CD3, CD20 patterns) derived from digitized slides to predict long-term metastasis risk. Data are drawn from cases treated between 2015 and 2025 at participating Chinese cancer centers and processed retrospectively for model development and validation.

Why it matters

Who can join: the full rules

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.

Where it's running

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

Study contacts

• Study coordinator: Jiaojiao Zhou
• Email: zhoujj@zju.edu.cn
• Phone: 0571-87784527

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.