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Bladder-PAD: Augmented Bladder Tumor Detection Using Real Time Based Artificial Intelligence

Sponsor
Centre Hospitalier Universitaire, Amiens (Other)
Overall Status
Recruiting
CT.gov ID
NCT05415631
Collaborator
(none)
500
Enrollment
1
Location
83.6
Anticipated Duration (Months)
6
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Today the standard for the diagnosis and monitoring of bladder tumors is bladder endoscopy. The performance of this exam is not perfect. With this work, based on artificial intelligence, the investigators wish to combine endoscopy with a complementary diagnostic tool in order to improve patient care. The main objective will be to reduce diagnostic errors / wanderings in patients treated or followed for bladder tumors, by imposing a new standard of diagnostic bladder mapping (high PPV and VPN, high precision)(primary purpose diagnostic). The secondary objective will be to homogenize and systematize the descriptive part of the lesions, and to use AI to better characterize tumor aggressiveness. The final objective being to validate a new precision tool (diagnostic companion) essential for developing and standardizing the therapeutic management of bladder tumors (correcting inter-observer heterogeneity).

In this project, video frame will be first extracted from our dataset of cystoscopy videos hosted in in the Next Cloud Recherche. Selected medical image will be segmented and analyzed using our pre-trained CNN model with a feature detection algorithm to obtain features.

Data will be analyzed on both patient and lesion levels. The study will assess the Bladder-PAD accuracy on the detection of bladder tumors, and its ability to predict tumor risk of recurrence and progression.

Condition or Disease Intervention/Treatment Phase

    Study Design

    Study Type:
    Observational
    Anticipated Enrollment :
    500 participants
    Observational Model:
    Case-Only
    Time Perspective:
    Prospective
    Official Title:
    Augmented Bladder Tumor Detection Using the Bladder-Portable Artifact Detection System: A Multicentric Prospective Analytic Study Using Real Time Based Artificial Intelligence (IA).
    Actual Study Start Date :
    May 13, 2022
    Anticipated Primary Completion Date :
    May 1, 2027
    Anticipated Study Completion Date :
    May 1, 2029

    Outcome Measures

    Primary Outcome Measures

    1. Tumor detection rate of white light cystoscopy [one day]

    2. Tumor detection rate of Bladder-PAD cystoscopy [one day]

    3. Tumor false detection rate of white light cystoscopy [one day]

    4. Tumor false detection rate of Bladder-PAD cystoscopy [one day]

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years and Older
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    Inclusion Criteria:

    • unifocal primary or recurrent suspected bladder cancer with tumor size less or equal than 3 cm

    • multifocal primary or recurrent suspected bladder cancer less or equal than 5 lesions and with tumor size less or equal than 3 cm.

    Exclusion Criteria:

    • Evidence of more than 5 tumors or more than 3 cm

    • computed tomography/cystoscopy suspect of muscle-invasive bladder cancer (cT2 or higher)

    • computed tomography/magnetic resonance evidence of distant metastases (lymphatic or organic)

    • Exclusion criteria will include gross hematuria and bacillus Calmette-Guerin (BCG) treatment or chemotherapy within 3 months of inclusion

    • An exception will be made if patients had received only a single course of chemotherapy immediately following TUR

    • Patients objecting to the use of their data in the context of research.

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Amiens University Hospital Amiens France 80054

    Sponsors and Collaborators

    • Centre Hospitalier Universitaire, Amiens

    Investigators

    None specified.

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Centre Hospitalier Universitaire, Amiens
    ClinicalTrials.gov Identifier:
    NCT05415631
    Other Study ID Numbers:
    • PI2022_843_0014
    First Posted:
    Jun 13, 2022
    Last Update Posted:
    Jul 15, 2022
    Last Verified:
    May 1, 2022
    Individual Participant Data (IPD) Sharing Statement:
    No
    Plan to Share IPD:
    No
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Centre Hospitalier Universitaire, Amiens
    Bladder Cancer
    cystoscopy
    Artificial Intelligence
    Additional relevant MeSH terms:
    Urinary Bladder Neoplasms

    Study Results

    No Results Posted as of Jul 15, 2022