Firstly, we retrospectively gathered the patient information who compliant with the criteria from 2012 to 2023, encompassing basic information, clinical information, along with MRI images, blood/urine samples, and tissue samples, for conducting relevant analyses of radiomics. Subsequently, based on artificial intelligence technology, deep learning and machine learning models were established on the basis of MRI radiomics and pathological histomics. Ultimately, the following research aims were accomplished: 1. Primary research objective: To explore the role of artificial intelligence and multimodal omics features in the staging and prognosis monitoring of bladder cancer. 2. Secondary objective: To explore the correlations among radiomics, case histomics, and test omics.
Study Type
OBSERVATIONAL
Enrollment
200
The First Affiliated Hospital with Nanjing Medical University
Nanjing, Jiangsu, China
Overall survival (OS)
Overall survival (OS) is defined as the duration from surgery to death or the date of the last follow-up.
Time frame: 2013-
Progression-free survival (PFS)
Progression-free survival (PFS) refers to the time from surgery until disease progression, the date of the last follow-up, or death from causes other than disease recurrence
Time frame: 2013-
Recurrence-Free Survival (RFS)
Recurrence-Free Survival (RFS)
Time frame: 2013-
Tumor Infiltration Status
Tumor Infiltration Status
Time frame: 2013-
Lymph node metastasis status
Lymph node metastasis status
Time frame: 2013-
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