Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence — COMORBID-AI  

Bladder Cancer Clinical Trial

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

Status Recruiting

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

Clinical Trial Description

n/a

Related Conditions & MeSH terms

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

• NCT number: NCT05204186
• Study type: Observational
• Source: Centre Hospitalier Universitaire, Amiens
• Contact: Fabien SAINT, Pr
• Phone: 03 22 45 59 52
• Email: saint-fabien@chu-amiens.fr
• Status: Recruiting
• Start date: January 10, 2021
• Completion date: January 2024