Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale

NCT07051083 | Recruiting

• 2 other identifiers: SYSKY-2023-1306-012024A03J1194
• Study Type: observational
• Target: 480
• Locations: 1 country
• Sites: 1

Brief Summary

Bladder cancer is the most common malignant tumor of the urinary system. The presence or absence of muscle invasion in early bladder cancer is an independent prognostic factor. The involvement of muscle invasion affects the choice of surgical methods and treatment. Preoperatively, the precise assessment of bladder cancer staging has important practical value. A more accurate preoperative assessment of bladder cancer staging can reduce overtreatment and provide a favorable basis for clinicians to choose more reasonable and effective surgical methods. Clinically, there has been a longstanding desire to diagnose the staging of bladder cancer through a simple, convenient, effective, and non-invasive examination. As relevant research progresses, a multi-omics diagnostic model will be beneficial in improving diagnostic efficiency. This project aims to establish a multi-omics artificial intelligence system based on deep learning and transfer learning to accurately diagnose the staging of bladder cancer and predict the efficacy of neoadjuvant chemotherapy. This system will assist in clinical treatment decision-making.

Conditions

Bladder Cancer; Staging; Deep Learning; Neoadjuvant Chemotherapy; Contrast Enhanced Ultrasound

Keywords

Bladder Cancer; Multi-omics; Staging; Deep Learning; Neoadjuvant Chemotherapy; Contrast Enhanced Ultrasound

Outcome Measures

Primary Outcomes (1)

• Detection of Muscle Invasion in Bladder Cancer

  To evaluate the preoperative staging of bladder cancer，whether it is non-muscle invasive bladder cancer (NMIBC) or muscle invasive bladder cancers (MIBC)

  Perform contrast-enhanced ultrasound (CEUS) examination within 2 weeks before the procedure.

Secondary Outcomes (1)

• Evaluate the Diagnostic Performance of Deep Learning and Transfer Learning in Preoperative Prediction of Muscle-Invasive Status in Bladder Cancer

  The patient should return for follow-up at 3 months postoperatively

Study Arms (3)

Training Group

Approximately 300 patients, which are used for the training dataset.

Diagnostic Test: Risk Stratification

Internal Validation Group

Approximately 100 patients, which are used for internal validation to assess the accuracy of the model.

Diagnostic Test: Risk Stratification

External Validation Group

Approximately 100 patients，which are used for external validation to assess the accuracy of the model.

Diagnostic Test: Risk Stratification

Interventions

Risk Stratification DIAGNOSTIC_TEST

Risk Stratification for Assessing Muscle Infiltration in Bladder Cancer.

External Validation Group; Internal Validation Group; Training Group

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

480 patients who were diagnosed with bladder cancer based on postoperative pathology at Sun Yat-sen Memorial Hospital, Sun Yat-sen University will be included between January 2023 and December 2026.

You may qualify if:

• Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients.
• The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found.
• No surgery or radiotherapy/chemotherapy has been performed.
• Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following:
• Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.);
• Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy;
• Rapid urine cytology and urine cytology FISH testing suggest malignancy.

You may not qualify if:

• Individuals unable to tolerate surgery;
• Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination;
• Unsuccessful preoperative ultrasound contrast examination or non-compliant patients;
• Postoperative pathology does not indicate bladder cancer;
• Patients who have undergone chemotherapy or radiation therapy.

Sponsors & Collaborators

• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: lead

Study Sites (1)

Sun Yat-sen Memorial Hospital, Sun Yat-sen University

Guangzhou, Guangdong, 510288, China

RECRUITING

Related Publications (13)

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MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Urinary Bladder Diseases; Urologic Diseases; Male Urogenital Diseases

Study Officials

• Qiyun Ou, Dr.

  Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Qiyun Ou, Dr.

CONTACT

(86)020-34071020; ouqy5@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 5 Years
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr.

Study Record Dates

• First Submitted: January 6, 2024
• First Posted: July 3, 2025
• Study Start: January 1, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: July 3, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will share
• Shared Documents: SAP, ANALYTIC CODE

Locations

CN (1)