Bladder CancerCOMORBID-AINCT05204186
Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence
An observational study of Bladder Cancer and Comorbidity, run by Centre Hospitalier Universitaire, Amiens at 1 site in France.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Bladder Cancer, Comorbidity, Deep Learning
- How many people
- 500 participants
- Where
- 1 siteFrance
- Study dates
- 12 Aug 2021to Dec 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This is a retrospective analysis of data from patients treated by radical cystectomy in our institution from 01 January 2006 to 01 January 2021. Qualitative and quantitative standard tumour data elements will be retrieved from medical files and certified General Cancer Registry. Data collection will be conducted from 9/2021 to 9/2022. Data management and analysis will be conducted from 1/2023 to 12/2024.
Who may be able to join
- 18 years and older
- Patient treated by radical cystectomy for bladder cancer
Who cannot join
- Computed tomography/magnetic resonance evidence of distant metastases.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site, 1 recruiting
- Recruiting
CHU Amiens Picardie
Amiens, Picardie, 80054
fabien saint, Pr, 03 22 45 59 52, saint-fabien@chu-amiens.fr
About this study
Official title: Evaluation of the Impact of COMORBIDities on Morbidity and Mortality After Radical Cystectomy for Cancer Using a Predictive Method With Artificial Intelligence
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.
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
- Conditions
- Bladder Cancer, Comorbidity, Deep Learning
- Keywords
- Bladder Cancer, Comorbidity, Deep learning, PSO-Particle swarm optimization
- Medical subject headings
- Urinary Bladder Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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) |
What is being measured
The primary outcome is the main question the study is designed to answer.
bladder cancer therapeutic choice as determined with this Artificial Intelligence predictive method
After retrieving associated comorbidities, any Grade 3, and over, Clavien-Dindo grading system complication rate (30dC and 90dC), information on primary treatment for bladder cancer (urothelial type and pT1 to pT4), outcome, time and cause of death, by our technician (from medical files of specific support centers), the primary objectives will be to model incorporation of comorbidities in making therapeutic choice, to improve care for patients with bladder cancer and specific guideline for treatment.
Measured: 90 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jan 2022
- Study start12 Aug 2021
- Primary completionDec 2028 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bladder CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centre Hospitalier Universitaire, Amiens
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05204186
- Sponsor's protocol ID
- PI2021_843_0176
- Acronym
- COMORBID-AI
- Status last verified
- Jun 2026
- First submitted
- 11 Jan 2022
- Last updated
- 15 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of the Impact of COMORBIDities on Morbidity and Mortality After Radical Cystectomy for Cancer Using a Predictive Method With Artificial Intelligence
- ClinicalTrials.gov ID
- NCT05204186
- Acronym / sponsor ID
- COMORBID-AI / PI2021_843_0176
- Conditions
- Bladder Cancer, Comorbidity, Deep Learning
- Keywords
- Bladder Cancer, Comorbidity, Deep learning, PSO-Particle swarm optimization
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Centre Hospitalier Universitaire, AmiensUniversity, hospital or other
- Study start
- 12 Aug 2021 (actual)
- Primary completion
- Dec 2028 (estimated)
- Study completion
- Dec 2028 (estimated)
- First posted
- 24 Jan 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This is a retrospective analysis of data from patients treated by radical cystectomy in our institution from 01 January 2006 to 01 January 2021. Qualitative and quantitative standard tumour data elements will be retrieved from medical files and certified General Cancer Registry. Data collection will be conducted from 9/2021 to 9/2022. Data management and analysis will be conducted from 1/2023 to 12/2024.
Key inclusion criteria (2 listed)
- 18 years and older
- Patient treated by radical cystectomy for bladder cancer
Key exclusion criteria (1 listed)
- Computed tomography/magnetic resonance evidence of distant metastases.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
bladder cancer therapeutic choice as determined with this Artificial Intelligence predictive method
Time frame: 90 days
After retrieving associated comorbidities, any Grade 3, and over, Clavien-Dindo grading system complication rate (30dC and 90dC), information on primary treatment for bladder cancer (urothelial type and pT1 to pT4), outcome, time and cause of death, by our technician (from medical files of specific support centers), the primary objectives will be to model incorporation of comorbidities in making therapeutic choice, to improve care for patients with bladder cancer and specific guideline for treatment.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jun 2026.
- Individual participant dataSharing planned: No
Related trials for Bladder Cancer
RecruitingPhase 2
Testing the Effectiveness of Two Immunotherapy Drugs (Nivolumab and Ipilimumab) With One Anti-cancer Targeted Drug (Cabozantinib) for Rare Genitourinary Tumors
Bladder Adenocarcinoma, Bladder Clear Cell Adenocarcinoma
Active, not recruitingPhase 1/2
A Beta-only IL-2 ImmunoTherapY Study
Advanced Solid Tumor, Unresectable Solid Tumor
Active, not recruitingPhase 2
Treating Muscle-invasive Bladder Cancer With A Non-surgical Method Consisting of Anti-PD-1 Therapy and Chemoradiation
Muscle-Invasive Bladder Carcinoma, Programmed Cell Death Protein 1 Inhibitor
RecruitingPhase 3
Trial of Nadofaragene Firadenovec vs. Observation in Participants With Intermediate Risk Non-Muscle Invasive Bladder Cancer
Intermediate Risk Non-Muscle Invasive Bladder Cancer
RecruitingPhase 3
A Trial to Evaluate Intravesical Nadofaragene Firadenovec Alone or in Combination With Chemotherapy or Immunotherapy in Participants With High-grade BCG Unresponsive Non-muscle Invasive Bladder Cancer
Non-muscle Invasive Bladder Cancer With Carcinoma in Situ
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Bladder Cancer and Comorbidity, run by Centre Hospitalier Universitaire, Amiens at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 15 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Fabien SAINT, Pr (03 22 45 59 52, saint-fabien@chu-amiens.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
bladder cancer therapeutic choice as determined with this Artificial Intelligence predictive method (time frame: 90 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jun 2026; AzTrials last refreshed its copy on 31 Oct 2026.