Breast Cancer
Conditions
Keywords
Breast Cancer, Artificial Intelligence, Distant Metastasis, Prediction
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.
Interventions
OTHERDiagnostic 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.
Sponsors
Second Affiliated Hospital, School of Medicine, Zhejiang University
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 95 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Predictive accuracy for distant metastasis risk assessed by Time-dependent Area Under the Receiver Operating Characteristic Curve (Time-dependent AUC) | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Sensitivity and Specificity | 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). |
| 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). | 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. |
| 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). | The agreement between the model-predicted probability of distant metastasis and the observed actual incidence will be visualized and assessed using a calibration curve. |
Countries
China
Contacts
CONTACTJiaojiao Zhou
Outcome results
None listed