Urothelial Carcinoma (UC)NCT07454941
Artificial Intelligence-assisted HER2 Expression Assessment in Urothelial Carcinoma Based on Imaging-pathology Omics
An observational study of Urothelial Carcinoma (UC) and HER2, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences at 1 site in China.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Urothelial Carcinoma (UC), HER2, Diagnostic
- How many people
- 4,000 participants
- Where
- 1 siteChina
- Study dates
- 2 Mar 2026to 3 Jun 2030
- How long will I take part?
- 1 Week
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: 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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
National Cancer Center / Cancer Hospital, Chinese Academy of Medical Sciences Beijing
Beijing, Chaoyang District, 100021
About this study
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.
Read the detailed description
- 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.
- Constructing an AI-Assisted HER2 Assessment and Prediction Model for Urothelial Carcinoma The core task is to integrate the selected multimodal features and construct a high-precision prediction model. We will explore various machine learning algorithms (such as XGBoost and Random Forest) and deep learning architectures, focusing on multimodal fusion strategies: including late-stage fusion that simply splices together radiomics, pathological deep features, and clinical features, and mid-stage fusion (such as using attention mechanisms) that involves feature interaction in the middle layers of the model, to capture deep complementary information between macroscopic imaging and microscopic pathology. The final output of the model is not a simple binary division, but rather provides a continuous probability value (0-1) for HER2 positivity, providing more decision-making basis for clinicians, thereby achieving non-invasive preoperative prediction. (4) Model Validation and Optimization To rigorously evaluate the model's generalization ability and clinical applicability, we performed cross-validation and hyperparameter optimization on an internal dataset, and conducted blinded testing. Evaluation metrics included statistical indicators such as sensitivity, specificity, positive predictive value, and negative predictive value. Simultaneously, interpretability technologies such as Grad-CAM were used to generate heatmaps to locate key imaging areas and pathological morphological features, enhancing physicians' understanding and trust in the model, and potentially uncovering new biological insights.
- Conditions
- Urothelial Carcinoma (UC), HER2, Diagnostic
- Keywords
- Urothelial Carcinoma, HER2, Artificial Intelligence, Diagnostic Model, Radiomics, Histopathology
- Medical subject headings
- Carcinoma, Transitional Cell, Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Patient diagnosed with urothelial carcinoma by pathology. | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, an average of 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2026
- Study start2 Mar 2026 (estimated)
- Primary completion3 Jun 2028 (estimated)
- Study completion3 Jun 2030 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Urothelial Carcinoma (UC)Studies of the same condition, open and under way.
- More trials run by Cancer Institute and Hospital, Chinese Academy of Medical SciencesThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 4,000 participants (estimated)
- Target duration
- 1 Week
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Collaborators
- Shanxi Province Cancer Hospital, Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center, RenJi Hospital, First Hospital of China Medical University, Huadong Hospital, Huaxi Hospital, The First Affiliated Hospital of Anhui Medical University, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- Responsible party
- Principal investigator, Xiongjun Ye, Cancer Institute and Hospital, Chinese Academy of Medical Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07454941
- Sponsor's protocol ID
- NCC5690
- Status last verified
- Mar 2026
- First submitted
- 2 Mar 2026
- Last updated
- 6 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 6 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-assisted HER2 Expression Assessment in Urothelial Carcinoma Based on Imaging-pathology Omics
- ClinicalTrials.gov ID
- NCT07454941
- Acronym / sponsor ID
- NCC5690
- Conditions
- Urothelial Carcinoma (UC), HER2, Diagnostic
- Keywords
- Urothelial Carcinoma, HER2, Artificial Intelligence, Diagnostic Model, Radiomics, Histopathology
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- Cancer Institute and Hospital, Chinese Academy of Medical SciencesUniversity, hospital or other
- Collaborators
- Shanxi Province Cancer Hospital, Cancer Hospital Chinese Academy of Medical Science, Shenzhen Center, RenJi Hospital, First Hospital of China Medical University, Huadong Hospital, Huaxi Hospital, The First Affiliated Hospital of Anhui Medical University, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
- Study start
- 2 Mar 2026 (estimated)
- Primary completion
- 3 Jun 2028 (estimated)
- Study completion
- 3 Jun 2030 (estimated)
- First posted
- 6 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
- 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.
- Constructing an AI-Assisted HER2 Assessment and Prediction Model for Urothelial Carcinoma The core task is to integrate the selected multimodal features and construct a high-precision prediction model. We will explore various machine learning algorithms (such as XGBoost and Random Forest) and deep learning architectures, focusing on multimodal fusion strategies: including late-stage fusion that simply splices together radiomics, pathological deep features, and clinical features, and mid-stage fusion (such as using attention mechanisms) that involves feature interaction in the middle layers of the model, to capture deep complementary information between macroscopic imaging and microscopic pathology. The final output of the model is not a simple binary division, but rather provides a continuous probability value (0-1) for HER2 positivity, providing more decision-making basis for clinicians, thereby achieving non-invasive preoperative prediction. (4) Model Validation and Optimization To rigorously evaluate the model's generalization ability and clinical applicability, we performed cross-validation and hyperparameter optimization on an internal dataset, and conducted blinded testing. Evaluation metrics included statistical indicators such as sensitivity, specificity, positive predictive value, and negative predictive value. Simultaneously, interpretability technologies such as Grad-CAM were used to generate heatmaps to locate key imaging areas and pathological morphological features, enhancing physicians' understanding and trust in the model, and potentially uncovering new biological insights.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Target follow-up
- 1 Week
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patient diagnosed with urothelial carcinoma by pathology. | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- 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.
Key inclusion criteria (6 listed)
- 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.
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Artificial intelligence predicts HER2 expression in urothelial carcinoma
Time frame: Through study completion, an average of 24 months
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Mar 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Urothelial Carcinoma (UC) and HER2, run by Cancer Institute and Hospital, Chinese Academy of Medical Sciences at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: enrolling by invitation, as of the sponsor's last update on 6 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Artificial intelligence predicts HER2 expression in urothelial carcinoma (time frame: Through study completion, an average of 24 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Mar 2026.