Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Bladder Cancer
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.This observational study evaluates a deep learning system that combines CT images and digitized pathology slides to predict outcomes in patients with bladder cancer. It includes a retrospective cohort and a prospective cohort of newly enrolled patients. The prospective cohort will assess the system in patients enrolled during (2024-2026), with follow-up for survival and recurrence.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study structure The study includes an initial retrospective phase and a subsequent prospective observational phase involving newly enrolled patients. The two phases are analyzed separately according to the specified analysis plan.
Retrospective phase The retrospective phase included patients from [2012-2024] and was used for model construction. Results from this phase were reported in npj series.
Prospective phase Enrollment of newly recruited patients began on [2024]. The prospective phase follows protocol, with ethics approval from the first affliated hospital of chongqing medical, university. Recruitment is consecutive, as specified in the protocol.
Model evaluation The prospective cohort is used for validation. The model version, preprocessing procedures and prediction thresholds used in this phase. Any model updating using this cohort is documented separately.
Registry update This update distinguishes the retrospective phase from the subsequently enrolled cohort. The prospective phase enrollment dates and the dates of protocol development and ethics approval are reported separately.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: QuanHao He
- Phone Number: 800-555-5555
- Email: 2020120460@stu.cqmu.edu.cn
Study Contact Backup
- Name: Mingzhao Xiao, PHD
- Phone Number: 800-555-5555
- Email: 2023140134@stu.cqmu.edu.cn
Study Locations
-
-
Chongqing Municipality
-
Chongqing, Chongqing Municipality, China, 400016
- Recruiting
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University
-
Contact:
- QuanHao He, PHD
- Phone Number: 800-555-5555
- Email: 2020120460@stu.cqmu.edu.cn
-
Contact:
- Mingzhao Xiao, PHD
- Phone Number: 023-89012557
- Email: 2023140134@stu.cqmu.edu.cn
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
We included patients who had surgery only or who had neoadjuvant chemotherapy before surgery. We excluded patients with a postoperative diagnosis of non-urothelial carcinoma.
Description
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
Study Plan
How is the study designed?
Design Details
- Observational Models: COHORT
- Time Perspectives: OTHER
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Retrospective cohort
Patients with bladder cancer included in the retrospective phase of the study, using clinical records, CT images, and whole-slide images obtained during [2012-2024]. This cohort was used for model development and/or validation and was reported in our npj publications. |
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images. |
|
Prospective cohort of newly enrolled patients
Newly enrolled patients with bladder cancer recruited at four centers between 2024 and2026. Eligibility is assessed at enrollment, and participants are followed according to protocol version and date for the study outcomes. This cohort is distinct from the previously reported retrospective cohort. |
develop and validate a deep learning system for prognostication prediction in bladder cancer based on CT radiomics and whole slide images. |
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Overall survival
Time Frame: 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. |
up to 10 years
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Recurrence free survival
Time Frame: 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 |
up to 10 years
|
|
progression-free survival
Time Frame: up to 10 years
|
The length of time during and after the treatment of a disease, such as cancer, that a patient lives with the disease but it does not get worse. In a clinical trial, measuring the progression-free survival is one way to see how well a new treatment works. Also called PFS. |
up to 10 years
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- BLCA_CMUFH
- K2024-187-01 (Other Identifier: The First Affiliated Hospital of Chongqing Medical University)
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
The datasets analyzed during the current study are not publicly available due to the privacy of patients but are available from the corresponding author on reasonable request.
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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