Clinical trial · Observational
Artificial Intelligence-assisted HER2 Expression Assessment in Urothelial Carcinoma Based on Imaging-pathology Omics
- 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)
This study aims to build upon previous research by using artificial intelligence methods to fuse multimodal data from imaging and pathology to construct a predictive model for HER2 expression in urothelial carcinoma. The model's performance will be validated and optimized using a multicenter cohort study, ultimately achieving accurate and rapid prediction of HER2 expression. This will guide precise decision-making for further HER2-targeted therapy and improve patient prognosis. Big data analysis and deep learning will also assist physicians in more accurately diagnosing the disease and developing personalized treatment plans. The research findings will promote the integration and development of artificial intelligence technology with the healthcare industry in the application of multimodal data from clinical, imaging, and pathology perspectives.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Diagnostic | — | UNRESOLVED | — |
| HER2 | — | UNRESOLVED | — |
| Urothelial Carcinoma (UC) | Urothelial Carcinoma | ONTOLOGY_EXACT | 0.85 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (1)
- label
- Patient diagnosed with urothelial carcinoma by pathology.
Primary outcomes (1)
- measure
- Artificial intelligence predicts HER2 expression in urothelial carcinoma
- timeFrame
- Through study completion, an average of 24 months
- description
- Based on artificial intelligence (AI) technology, this study aims to establish a predictive model by quantitatively mapping the correlation between annotated whole-section images of urothelial carcinoma and MRI scans, identifying common characteristics, and ultimately building a predictive model. Firstly, this model can accurately assess the HER2 status of bladder cancer, eliminating the need for immunohistochemistry to obtain detailed pathological information. Secondly, the established AI predictive model can accurately diagnose the benign or malignant, invasive, grade, and subtype of bladder cancer by predicting the subject's MRI images before biopsy or surgery.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Age ≥ 18 years. * Patients pathologically diagnosed with urothelial carcinoma. * Possession of pre-biopsy or pre-operative multiparametric MRI raw data. * Possession of corresponding paraffin-embedded tissue blocks and digital whole-section images. * Possession of HER2 status report confirmed by immunohistochemistry. * Signed informed consent form. Exclusion Criteria: * Contraindications to MRI, such as presence of metallic implants or claustrophobia. * Patients with missing baseline clinical or pathological information. * Patients who have received neoadjuvant therapy. * Patients with a history of other malignant tumors. * Patients with mixed or non-urothelial carcinoma pathology.
References
Publications (0)
Data not yet available