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

Study Overview
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Brief Summary
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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

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

Conditions
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Bladder Cancer, Comorbidity, Deep Learning

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: 18 years and older Patient treated by radical cystectomy for bladder cancer Exclusion Criteria: Computed tomography/magnetic resonance evidence of distant metastases.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Group A<br>Patient with (Group A) any Grade 3 (and over) Clavien-Dindo grading complication rate (30dC and 90dC) | |
| Group B<br>Patient without (Group B) any Grade 3 (and over) Clavien-Dindo grading complication rate (30dC and 90dC) | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Bladder Cancer, Comorbidity, Deep learning, PSO-Particle swarm optimization