Bladder Cancer
Conditions
Keywords
Multimodal omics features, artificial intelligence, staging and prognostic models, Bladder cancer
Brief summary
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.
Interventions
None listed
Sponsors
Suzhou Municipal Hospital
Yixing People's Hospital
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Huai an First People Hospital
Jiangsu Province Hospital of Traditional Chinese Medicine
The First Affiliated Hospital of Zhengzhou University
The second affiliated hospital of Xuzhou medical university
The First Affiliated Hospital with Nanjing Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* 1\. Patients with bladder cancer in preoperative examination; 2. Gender is not limited; 3. Age≥ 18 years old; 4. Be able to provide MRI images, pathological data and laboratory examination data before the operation; 5. Agree to provide basic personal clinical information and pathological and imaging data for scientific research use, and sign the informed consent form; 6. Agree to provide monitoring results during follow-up recurrence monitoring;
Exclusion criteria
* 1\. Incomplete clinicopathological data; 2. Combined with upper tract urothelial carcinoma or previously diagnosed upper tract urothelial carcinoma; 3. Is participating in the rest of the clinical studies; Unable to cooperate with the relevant examinations of this project, and do not agree to sign the informed consent form.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overall survival (OS) | 2013- | Overall survival (OS) is defined as the duration from surgery to death or the date of the last follow-up. |
| Progression-free survival (PFS) | 2013- | 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 |
| Recurrence-Free Survival (RFS) | 2013- | Recurrence-Free Survival (RFS) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Tumor Infiltration Status | 2013- | Tumor Infiltration Status |
| Lymph node metastasis status | 2013- | Lymph node metastasis status |
Other
| Measure | Time frame | Description |
|---|---|---|
| Neoadjuvant and Adjuvant Treatment Effects | 2013- | Neoadjuvant and Adjuvant Treatment Effects |
Countries
China
Outcome results
None listed