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.
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.
Clinical symptoms consistent with suspected bladder cancer (such as gross hematuria, etc.);
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;
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.
Cohort
Prospective
Risk Stratification
Diagnostic testRisk Stratification for Assessing Muscle Infiltration in Bladder Cancer.
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)
Evaluate the Diagnostic Performance of Deep Learning and Transfer Learning in Preoperative Prediction of Muscle-Invasive Status in Bladder Cancer
The diagnostic performance of the Deep Learning and Transfer Learning Model was primarily evaluated using the statistical metrics of AUC (Area Under the ROC Curve), sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV).
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