Clinical trial · Observational
A Study on Predicting the Risk of Distant Metastasis in Breast Cancer Using AI-Generated Spatial Pathological Maps
NCT07244094CI-TRIAL-00103358ARGUS projectrecruitingClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Breast Cancer | Malignant Breast Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Diagnostic Test: AI-Based Spatial Pathomic Analysis | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- Patients with primary breast cancer who have experienced distant metastasis outcomes within 5 years
- interventionNames
- Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis
- label
- Patients with primary breast cancer who have not experienced distant metastasis for at least 5 years
- interventionNames
- Other: Diagnostic Test: AI-Based Spatial Pathomic Analysis
Primary outcomes (1)
- measure
- Predictive accuracy for distant metastasis risk assessed by Time-dependent Area Under the Receiver Operating Characteristic Curve (Time-dependent AUC)
- timeFrame
- From the date of initial surgery up to 5 years post-operatively, with the occurrence of distant metastasis defined as the event of interest.
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- Female
- Minimum age
- 18 Years
- Maximum age
- 95 Years
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available
No reference posted for this study.