CancerNCT06909643
Development and Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer
An observational study of Cancer and Neoadjuvant Therapy, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University at 1 site in China.
Trial summary
This study is a multi-center observational study without interventions, including the construction of an AI predictive model, with retrospective and prospective testing. The study participants are bladder cancer patients who have undergone imaging examinations, been pathologically diagnosed, and received neoadjuvant… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Cancer, Neoadjuvant Therapy
- What is being tested?
- Artificial intelligence (AI)-based predictive model
- How many people
- 469 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2022to 31 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with pathologically confirmed bladder cancer who undergo neoadjuvant therapy and radical cystectomy are planned to be enrolled in this diagnostic test to assess the model's clinical application capability.
Who may be able to join
- Bladder occupying lesions, with histopathological confirmation of bladder cancer after resection.
- Planned neoadjuvant therapy and radical cystectomy.
Who cannot join
- Patients who have not undergone standard bladder imaging examinations or have missing imaging or pathological data.
- Patients who have received local treatments (such as interventional embolization) or systemic treatments (such as radiotherapy, chemotherapy, immunotherapy, or targeted therapy).
- Poor quality of imaging or pathological images.
Trial locations and contacts
1 site in 1 country.
China1 site
Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Guangzhou, Guangdong, 510080
About this study
This study is a multi-center observational study without interventions, including the construction of an AI predictive model, with retrospective and prospective testing. The study participants are bladder cancer patients who have undergone imaging examinations, been pathologically diagnosed, and received neoadjuvant treatment, with complete clinical and pathological data. The study plans to enroll 130 patients from our center, collecting corresponding imaging images, and gathering clinical and genomic data to build and internally validate a multimodal AI model. The model's generalization and robustness will be tested to explore the association between multimodal data and the efficacy of neoadjuvant treatment for bladder cancer. The aim is to assist clinicians in predicting and evaluating the efficacy of neoadjuvant treatment for bladder cancer, with the goal of improving patient diagnosis, treatment outcomes, and prognosis.
Read the detailed description
Bladder cancer is one of the most common malignancies of the genitourinary system worldwide. For muscle - invasive bladder cancer amenable to radical resection, the standard treatment is neoadjuvant therapy combined with radical cystectomy, with neoadjuvant therapy playing a crucial role. Currently, numerous studies have shown that cisplatin - based neoadjuvant chemotherapy can downstage tumors, reduce the risk of mortality in bladder cancer patients, improve survival rates, and enhance prognosis. Other treatment approaches such as neoadjuvant immunotherapy, targeted therapy, and combination therapies are also under investigation. However, responses to neoadjuvant therapy vary among bladder cancer patients, with some not achieving the desired therapeutic goals. Therefore, accurately predicting participants' response to treatment can provide an important reference for personalized and precise treatment of bladder cancer.
In recent years, as advancements in computational power and data storage capacity, artificial intelligence (AI) has been widely applied in the field of digital diagnostics. AI technologies can extract and integrate a large number of features from multimodal data such as pathology, imaging, and clinical records, enabling precise disease diagnosis, prognosis assessment, and treatment prediction. In the field of tumor treatment prediction, multimodal AI technologies have achieved numerous breakthroughs, developing efficacy prediction models for tumors such as rectal and breast cancer based on imaging and pathological data, and validating the models' generalization capabilities through external validation.
Therefore, the investigators plan to construct and validate a "Bladder Cancer Neoadjuvant Treatment Efficacy Prediction Model" based on multimodal data (including MRI images, digital pathology images, and clinical records) of bladder cancer patients, and develop an AI-assisted prediction software for neoadjuvant treatment efficacy in bladder cancer. The study will adopt a combined retrospective and prospective data collection design to ensure sufficient sample size and model robustness. This study plans to enroll a total of 550 patients, including 500 retrospective cases and 50 prospective cases. Based on sample size calculation formulas and preliminary study results, the minimum sample size required for model development is estimated to be 220 cases. Considering imaging quality control, internal validation, and external validation needs, we plan to retrospectively enroll 500 cases from both the local center and external institutions. Due to the relative rarity of bladder cancer patients receiving neoadjuvant therapy, 50 prospective cases planned to verify the model's performance and clinical applicability.
- Conditions
- Cancer, Neoadjuvant Therapy
- Being tested
- Artificial intelligence (AI)-based predictive model
- Keywords
- Artificial Intelligence, Multimodal Fusion, Therapy Prediction, Magnetic Resonance Imaging, Digital Pathology
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence (AI)-based predictive modelDiagnostic test
Collect magnetic resonance imaging and pathological slides of resected tumor of the enrolled patients. Analyze the data using the AI model to generate predictive results (sensitive or insensitive to the neoadjavant therapy). No intervention to patients would be performed in this diagnostic test study.
| Study group | Role | What they receive |
|---|---|---|
| Patients with bladder cancer undergoing neoadjuvant therapy | Patients pathological diagnosed with bladder cancer undergoing neoadjuvant therapy. Receives: Diagnostic Test: Artificial intelligence (AI)-based predictive model |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC (Area Under the Receiver Operating Characteristic Curve)
A comprehensive metric reflecting the overall discriminative ability of the AI model, which integrates the model's sensitivity and specificity across all possible threshold values. It quantifies the probability that the model will correctly rank a randomly selected therapy-sensitive patient higher than a randomly selected therapy-insensitive patient.
Measured: For each enrolled patient, the AI model's prediction results will be generated within several days after neoadjuvant therapy. The AUC of the model will be evaluated upon study completion, an average of 3 years.
Secondary outcomes (2)
sensitivity
the number of correctly diagnosed positive patient (sensitive to therapy), to be divided by the number of patients in total.
Measured: For each enrolled patient, the predictive results of AI model will be obtained in several days after neoadjuvant therapy, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.
specificity
the number of correctly diagnosed negative patients (therapy insensitive), to be divided by the number of negative patients in total.
Measured: For each enrolled patient, the predictive results of AI model will be obtained in several days after neoadjuvant therapy, and the specificity of the AI model will be evaluated through study completion, an average of 3 year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Apr 2025
- Study start1 Jan 2022
- Primary completion31 Dec 2025
- Study completion31 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CancerStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Other
- Enrolment
- 469 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06909643
- Sponsor's protocol ID
- SYSKY-2024-738-01
- Status last verified
- Apr 2025
- First submitted
- 19 Mar 2025
- Last updated
- 3 Jun 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: To protect patient privacy, magnetic resonance imaging, pathological slide images and other patient-related data are not publicly accessible.
Study at a glance
From the registry record, last updated by the sponsor on 3 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Multimodal Fusion Artificial Intelligence Model for Predicting the Efficacy of Neoadjuvant Treatment of Bladder Cancer
- ClinicalTrials.gov ID
- NCT06909643
- Acronym / sponsor ID
- SYSKY-2024-738-01
- Conditions
- Cancer, Neoadjuvant Therapy
- Keywords
- Artificial Intelligence, Multimodal Fusion, Therapy Prediction, Magnetic Resonance Imaging, Digital Pathology
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 469 participants (actual)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 31 Dec 2025 (actual)
- Study completion
- 31 Dec 2025 (actual)
- First posted
- 3 Apr 2025 (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 is a multi-center observational study without interventions, including the construction of an AI predictive model, with retrospective and prospective testing. The study participants are bladder cancer patients who have undergone imaging examinations, been pathologically diagnosed, and received neoadjuvant treatment, with complete clinical and pathological data. The study plans to enroll 130 patients from our center, collecting corresponding imaging images, and gathering clinical and genomic data to build and internally validate a multimodal AI model. The model's generalization and robustness will be tested to explore the association between multimodal data and the efficacy of neoadjuvant treatment for bladder cancer. The aim is to assist clinicians in predicting and evaluating the efficacy of neoadjuvant treatment for bladder cancer, with the goal of improving patient diagnosis, treatment outcomes, and prognosis.
Detailed description
Bladder cancer is one of the most common malignancies of the genitourinary system worldwide. For muscle - invasive bladder cancer amenable to radical resection, the standard treatment is neoadjuvant therapy combined with radical cystectomy, with neoadjuvant therapy playing a crucial role. Currently, numerous studies have shown that cisplatin - based neoadjuvant chemotherapy can downstage tumors, reduce the risk of mortality in bladder cancer patients, improve survival rates, and enhance prognosis. Other treatment approaches such as neoadjuvant immunotherapy, targeted therapy, and combination therapies are also under investigation. However, responses to neoadjuvant therapy vary among bladder cancer patients, with some not achieving the desired therapeutic goals. Therefore, accurately predicting participants' response to treatment can provide an important reference for personalized and precise treatment of bladder cancer.
In recent years, as advancements in computational power and data storage capacity, artificial intelligence (AI) has been widely applied in the field of digital diagnostics. AI technologies can extract and integrate a large number of features from multimodal data such as pathology, imaging, and clinical records, enabling precise disease diagnosis, prognosis assessment, and treatment prediction. In the field of tumor treatment prediction, multimodal AI technologies have achieved numerous breakthroughs, developing efficacy prediction models for tumors such as rectal and breast cancer based on imaging and pathological data, and validating the models' generalization capabilities through external validation.
Therefore, the investigators plan to construct and validate a "Bladder Cancer Neoadjuvant Treatment Efficacy Prediction Model" based on multimodal data (including MRI images, digital pathology images, and clinical records) of bladder cancer patients, and develop an AI-assisted prediction software for neoadjuvant treatment efficacy in bladder cancer. The study will adopt a combined retrospective and prospective data collection design to ensure sufficient sample size and model robustness. This study plans to enroll a total of 550 patients, including 500 retrospective cases and 50 prospective cases. Based on sample size calculation formulas and preliminary study results, the minimum sample size required for model development is estimated to be 220 cases. Considering imaging quality control, internal validation, and external validation needs, we plan to retrospectively enroll 500 cases from both the local center and external institutions. Due to the relative rarity of bladder cancer patients receiving neoadjuvant therapy, 50 prospective cases planned to verify the model's performance and clinical applicability.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with bladder cancer undergoing neoadjuvant therapy | – | Diagnostic Test: Artificial intelligence (AI)-based predictive modelPatients pathological diagnosed with bladder cancer undergoing neoadjuvant therapy. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with pathologically confirmed bladder cancer who undergo neoadjuvant therapy and radical cystectomy are planned to be enrolled in this diagnostic test to assess the model's clinical application capability.
Key inclusion criteria (2 listed)
- Bladder occupying lesions, with histopathological confirmation of bladder cancer after resection.
- Planned neoadjuvant therapy and radical cystectomy.
Key exclusion criteria (3 listed)
- Patients who have not undergone standard bladder imaging examinations or have missing imaging or pathological data.
- Patients who have received local treatments (such as interventional embolization) or systemic treatments (such as radiotherapy, chemotherapy, immunotherapy, or targeted therapy).
- Poor quality of imaging or pathological images.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence (AI)-based predictive modelCollect magnetic resonance imaging and pathological slides of resected tumor of the enrolled patients. Analyze the data using the AI model to generate predictive results (sensitive or insensitive to the neoadjavant therapy). No intervention to patients would be performed in this diagnostic test study. | Diagnostic test | Patients with bladder cancer undergoing neoadjuvant therapy |
Primary outcome
AUC (Area Under the Receiver Operating Characteristic Curve)
Time frame: For each enrolled patient, the AI model's prediction results will be generated within several days after neoadjuvant therapy. The AUC of the model will be evaluated upon study completion, an average of 3 years.
A comprehensive metric reflecting the overall discriminative ability of the AI model, which integrates the model's sensitivity and specificity across all possible threshold values. It quantifies the probability that the model will correctly rank a randomly selected therapy-sensitive patient higher than a randomly selected therapy-insensitive patient.
2 secondary outcomes
sensitivity
Time frame: For each enrolled patient, the predictive results of AI model will be obtained in several days after neoadjuvant therapy, and the sensitivity of the AI model will be evaluated through study completion, an average of 3 year.
specificity
Time frame: For each enrolled patient, the predictive results of AI model will be obtained in several days after neoadjuvant therapy, and the specificity of the AI model will be evaluated through study completion, an average of 3 year.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Jun 2026.
- Individual participant dataSharing planned: No. To protect patient privacy, magnetic resonance imaging, pathological slide images and other patient-related data are not publicly accessible.
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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. This study is not recruiting at the moment.
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 Cancer and Neoadjuvant Therapy, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University 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 2 inclusion and 3 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: finished, as of the sponsor's last update on 3 Jun 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
AUC (Area Under the Receiver Operating Characteristic Curve) (time frame: For each enrolled patient, the AI model's prediction results will be generated within several days after neoadjuvant therapy. The AUC of the model will be evaluated upon study completion, an average of 3 years.). 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 3 Jun 2026.