An observational study in Urothelial Carcinoma (UC), HER2 and Diagnostic, sponsored by Cancer Institute and Hospital, Chinese Academy of Medical Sciences. Enrolling by invitation at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-03-06.
Sponsored by Cancer Institute and Hospital, Chinese Academy of Medical Sciences · Observational
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.
Patient Data Collection and Construction of a Multimodal Dataset and Sample Library
This study will construct a standardized, high-quality multimodal urothelial carcinoma data warehouse. The core is to collect patient data from those pathologically diagnosed with urothelial carcinoma who possess preoperative multiparametric MRI (T1WI, T2WI, DWI/ADC) and paired digital H\&E whole slide images (WSI). All cases must use expert-reviewed immunohistochemical results as the gold standard label for HER2 status, and be accompanied by complete clinical data. We will establish strict inclusion and exclusion criteria to ensure data quality, and utilize a professional platform to de-identify, standardize, and correlate clinical, pathological, and imaging data for storage, laying a solid data foundation for subsequent analysis.
Extraction and Screening of Imaging/Pathological Features of Urothelial Carcinoma Patients
This stage aims to extract quantitative features from macroscopic imaging and microscopic pathological images. For MRI, radiologists will manually delineate the three-dimensional region of the tumor (VOI), and then use radiomics tools to extract a large number of quantitative features describing tumor intensity, shape, and texture heterogeneity. For H\&E pathological sections, a deep learning/machine learning model was used to automatically segment the tumor region. High-dimensional deep features from millions of image patches were extracted using pre-trained convolutional neural networks, and then aggregated into a feature vector for the entire section using a multi-instance learning framework. Finally, statistical tests and learning methods such as LASSO were combined to select the most relevant and stable feature subset for HER2 status assessment, eliminating redundancy and providing input for model construction.
716 studies on the registry are indexed under Carcinoma, Transitional Cell; 201 are open to participants now.
This study's planned enrollment of 4,000 is above the median of 200 across 123 observational studies indexed under Carcinoma, Transitional Cell.
Browse Carcinoma, Transitional Cell studies →Cancer Institute and Hospital, Chinese Academy of Medical Sciences is the lead sponsor of 373 studies on the registry; 270 are open to participants now.
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We collected imaging and pathological data from patients diagnosed with urothelial carcinoma. Using artificial intelligence, we fused multimodal data from imaging and pathology to construct a predictive model for HER2 expression in urothelial carcinoma. The model's performance was validated and optimized using a multi-center 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.
Exclusion Criteria:
Artificial intelligence predicts HER2 expression in urothelial carcinoma
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.
Time frame: Through study completion, an average of 24 months
Plan to share: Undecided
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Cancer Institute and Hospital, Chinese Academy of Medical Sciences