Bladder Cancer
Conditions
Keywords
deep learning, Radiomics, Histopathological tissue slides, Tomography
Brief summary
Bladder cancer (BLCA), with its diverse histopathological features and varying patient outcomes, poses significant challenges in diagnosis and prognosis. Postoperative survival stratification based on radiomics feature and whole slide image feature may be useful for treatment decisions to improve prognosis. In this research, we aim to develop a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with BLCA.
Detailed description
Bladder cancer can be difficult to diagnose and predict outcomes for, as the disease can vary greatly between patients. This research aims to develop a new system that uses artificial intelligence to analyze patient information, including images from surgery and scans. This system could then automatically predict a patient's overall survival and how likely they are to survive specifically from bladder cancer. This information could be used by doctors to make better treatment decisions for each patient.
Interventions
OTHERDeep learning system for prognostication prediction in bladder cancer
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images.
Sponsors
Mingzhao Xiao
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* patients with bladder cancer who had surgery like radical cystectomy or transurethral resection of bladder tumour (TURBT) * contrast-CT scan less than two weeks before surgery * complete CT image data and clinical data * complete whole slide image data
Exclusion criteria
* patients with a postoperative diagnosis of non-urothelial carcinoma * poor quality of CT images * incomplete clinical and follow-up data
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overall survival | up to 10 years | the time from the date of surgery to death from any cause or the date of last contact (censored observation) at the date of data cut-off. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Recurrence free survival | up to 10 years | the time from the date of surgery to the date of first documented disease recurrence. Patients without recurrence at the time of analysis will be censored |
Countries
China
Contacts
Primary ContactQuanHao He
Backup ContactMingzhao Xiao, PHD
Outcome results
None listed