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

NCT05204186 | Unknown

• 1 other identifier: PI2021_843_0176
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

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

Conditions

Bladder Cancer; Comorbidity; Deep Learning

Keywords

Bladder Cancer; Comorbidity; Deep learning; PSO-Particle swarm optimization

Outcome Measures

Primary Outcomes (1)

• 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

Study Arms (2)

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)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• 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.

You may qualify if:

• years and older
• Patient treated by radical cystectomy for bladder cancer

You may not qualify if:

• Computed tomography/magnetic resonance evidence of distant metastases.

Sponsors & Collaborators

• Centre Hospitalier Universitaire, Amiens: lead

Study Sites (1)

CHU Amiens Picardie

Amiens, Picardie, 80054, France

RECRUITING

MeSH Terms

Conditions

Urinary Bladder Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Urinary Bladder Diseases; Urologic Diseases; Male Urogenital Diseases

Central Study Contacts

Fabien SAINT, Pr

CONTACT

03 22 45 59 52; saint-fabien@chu-amiens.fr

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 11, 2022
• First Posted: January 24, 2022
• Study Start: January 10, 2021
• Primary Completion: January 1, 2024
• Study Completion: January 1, 2024
• Last Updated: February 8, 2023

Record last verified: 2023-02

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)