RECRUITINGOBSERVATIONAL

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

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

About This Trial

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.

Who May Be Eligible (Plain English)

Who May Qualify: 1. Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients. 2. The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found. 3. No surgery or radiotherapy/chemotherapy has been performed. 4. Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following: 1. Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.); 2. Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy; 3. Rapid urine cytology and urine cytology FISH testing suggest malignancy. Who Should NOT Join This Trial: 1. Individuals unable to tolerate surgery; 2. Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination; 3. Unsuccessful preoperative ultrasound contrast examination or non-compliant patients; 4. Postoperative pathology does not indicate bladder cancer; 5. Patients who have undergone chemotherapy or radiation therapy. Always talk to your doctor about whether this trial is right for you.

Original Eligibility Criteria

View original clinical language

Inclusion Criteria: 1. Ultrasound and other imaging examinations (CT, MR, etc.) suggest bladder masses and are suspicious for bladder cancer patients. 2. The bladder is well filled, and no allergic reactions to ultrasound contrast agents are found. 3. No surgery or radiotherapy/chemotherapy has been performed. 4. Patients who meet the indications for surgical resection and are planned for surgical treatment, including one of the following: 1. Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.); 2. Patients with confirmed primary or recurrent bladder cancer by cystoscopic biopsy; 3. Rapid urine cytology and urine cytology FISH testing suggest malignancy. Exclusion Criteria: 1. Individuals unable to tolerate surgery; 2. Individuals allergic to ultrasound contrast agents, unable to undergo ultrasound contrast examination; 3. Unsuccessful preoperative ultrasound contrast examination or non-compliant patients; 4. Postoperative pathology does not indicate bladder cancer; 5. Patients who have undergone chemotherapy or radiation therapy.

Treatments Being Tested

DIAGNOSTIC_TEST

Risk Stratification

Risk Stratification for Assessing Muscle Infiltration in Bladder Cancer.

Locations (1)

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

Guangzhou, Guangdong, China