Clinical trial NCT05204186: Impact of COMORBIDities After Radical Cystectomy Using a Predictive Method With Artificial Intelligence
Official title: Evaluation of the Impact of COMORBIDities on Morbidity and Mortality After Radical Cystectomy for Cancer Using a Predictive Method With Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Bladder Cancer
Comorbidity
Deep Learning
Keywords: Bladder Cancer
Comorbidity
Deep learning
PSO-Particle swarm optimization
Lead sponsor: Centre Hospitalier Universitaire, Amiens
Sponsor class: OTHER
First posted: 2022-01-24
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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.

Brief 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


Eligibility criteria: Inclusion Criteria:

* 18 years and older
* Patient treated by radical cystectomy for bladder cancer

Exclusion Criteria:

* Computed tomography/magnetic resonance evidence of distant metastases.

Primary outcomes:
measure: bladder cancer therapeutic choice as determined with this Artificial Intelligence predictive method
timeFrame: 90 days
Secondary outcomes:
Other outcomes:
Locations:
city: Amiens
country: France
facility: CHU Amiens Picardie
state: Picardie
Study officials:
References: