Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence

Bladder Cancer Clinical Trial

— COMORBID-AI  

Official title:

Evaluation of the Impact of COMORBIDities on Morbidity and Mortality After Radical Cystectomy for Cancer Using a Predictive Method With Artificial Intelligence

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05204186
• Other study ID #: PI2021_843_0176
• Status: Recruiting
• Start date: January 10, 2021
• Est. completion date: January 2024

Study information

• Verified date: February 2023
• Source: Centre Hospitalier Universitaire, Amiens
• Contact: Fabien SAINT, Pr
• Phone: 03 22 45 59 52
• Email: saint-fabien[@]chu-amiens.fr
• Is FDA regulated: No
• Study type: Observational

Clinical 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 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

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: January 2024
• Est. primary completion date: January 2024
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• 18 years and older

• Patient treated by radical cystectomy for bladder cancer

Exclusion Criteria:

• Computed tomography/magnetic resonance evidence of distant metastases.

Related Conditions & MeSH terms

• Bladder Cancer
• Comorbidity
• Deep Learning
• Urinary Bladder Neoplasms

Locations

Country	Name	City	State
France	CHU Amiens Picardie	Amiens	Picardie

Sponsors (1)

Lead Sponsor	Collaborator
Centre Hospitalier Universitaire, Amiens

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	90 days