Studies how existing health conditions affect outcomes after bladder cancer surgery
Uses artificial intelligence to study how other illnesses affect adults after bladder removal surgery.
Key facts
- Who runs it
- Centre Hospitalier Universitaire, Amiens
- Where
- Amiens, FranceSee the site
- Trial code
- Registry title
- Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence
- Status
- Recruiting
- People taking part
- 500 people, planned
- Started
- 12 Aug 2021
- Expected to end
- Dec 2028
- Type of study
- Observational, 2 groups
- Record updated
- 15 June 2026
Who can take part
AI summary
From the trial's eligibility criteria. Machine-read from the registry text on this page, so it can be wrong. The registry text below and the trial team decide who can take part.
The trial is looking for adults who had their bladder removed (radical cystectomy) to treat bladder cancer.
- Age
- 18 years and older
- Sex
- All sexes
- Disease stage
- Bladder cancer treated by radical cystectomy
- Earlier treatment
- Must have been treated by radical cystectomy for bladder cancer
Main reasons for not taking part
- Distant metastases seen on CT or MRI scan
The registry criteria, word for word
Show all 3 criteriaShow less
Who can take part
- 18 years and older
- Patient treated by radical cystectomy for bladder cancer
Who cannot take part
- Computed tomography/magnetic resonance evidence of distant metastases
Where in France
- Chu Amiens PicardieAmiens, FranceContact: fabien saint, PrTaking people
About the trial
What the trial wants to find out
Clinician and the multidisciplinary team meeting in oncologic urology (MMO) play a key-role in the decision making. An unexplained surgeon attributable variance, probably linked to the subjective "eyeball test" effect, was identified as a strongest factor underlying non-compliance with guide line recommendations in the management of bladder cancer. So high-quality studies that identify barriers and modulators (such as comorbidities) of provider-level adoption of guidelines and how comorbidities are associated in making therapeutic choice and their impact in bladder cancer specific survival and overall survival, are crucial. To identify patients at high risk of early death, and to improve specific guideline for treatment might be decisive.
Read full original descriptionShow less
In order to assess survival, where mortality events compete, it will be more appropriate to compute a Cumulative Incidence Function (namely CIF). The investigators will compare outcomes across patient populations to obtain information to improve clinical decision-making. Such learning will be done through the use of neural networks or by applying population-based approaches, such as Genetic Algorithms (GA), Ant Colony Systems (ACS) and Particle Swarm Optimization (PSO), using as a four-stage based approach.
First, the investigators propose a "pretopology space" in order to study a dynamic phenomenon. Second, the investigators recall that the K-means approach remains one of the most used approaches for classifying a set of elements (patients / persons / others) into K (disjunctive) clusters. Third, the investigators propose a learning pretopology space for enhancing the clustering. Such an approach can be assimilated in spirit to one applied with high success on deep learning. Fourth and last, the investigators propose a reactive method that is able to include some new elements or remove some contained elements
The 2 groups in the trial
- Group A
Patient with (Group A) any Grade 3 (and over) Clavien-Dindo grading complication rate (30dC and 90dC)
- Group B
Patient without (Group B) any Grade 3 (and over) Clavien-Dindo grading complication rate (30dC and 90dC)
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Contact the study team: see the registry record → Contacts and Locations
Print a page for my doctorData and sources
- Registry record
- Listed on ClinicalTrials.gov.
- Official title
- Evaluation of the Impact of COMORBIDities on Morbidity and Mortality After Radical Cystectomy for Cancer Using a Predictive Method With Artificial Intelligence
- Plain-language title
- Studies how existing health conditions affect outcomes after bladder cancer surgeryGenerated from the registry record. The registry title is the official one.
- AI summary
- From the trial's eligibility criteria. Read by machine on 12 November 2026.
- Record updated
- 15 June 2026