Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
Recruiting
Observational Study
Bladder Cancer
No Placebo Group
Every participant receives an active treatment — no one gets a placebo.
No Study Drug
Researchers observe your health over time — no experimental treatment is given.
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At a Glance
Sex
Any
Study type
Observational
Participants needed
1,000 (estimated)
Sponsor
Mingzhao Xiao · Other
Who this trial is looking for
This trial is looking for patients with bladder cancer who have had surgery. Participating may help improve predictions of survival based on medical images and data.
Are You a Good Fit for This Trial?
You may be able to join if
I have been diagnosed with bladder cancer
I have had bladder surgery like a radical cystectomy or TURBT
I had a contrast-CT scan less than two weeks before my surgery
I have complete CT image and clinical data
I have complete whole slide image data
You may not be able to join if
I have a postoperative diagnosis of non-urothelial carcinoma
I have poor quality CT images
I have incomplete clinical and follow-up data
Summarized in plain language from this trial's official eligibility criteria.
The full criteria are further down this page — only the research team can
confirm whether you qualify.
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Answer a few quick questions to see if you may meet the eligibility requirements.
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 …
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.
Trial Locations
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Eligibility Criteria
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:
* …
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
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