Augmented Bladder Tumor Detection Using Real Time Based Artificial Intelligence

NCT05415631 | Recruiting

• 1 other identifier: PI2022_843_0014
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

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.

Conditions

Bladder Cancer; Cystoscopy; Artificial Intelligence

Keywords

Bladder Cancer; cystoscopy; Artificial Intelligence

Outcome Measures

Primary Outcomes (4)

• Tumor detection rate of white light cystoscopy

  one day

• Tumor detection rate of Bladder-PAD cystoscopy

  one day

• Tumor false detection rate of white light cystoscopy

  one day

• Tumor false detection rate of Bladder-PAD cystoscopy

  one day

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

patients treated for bladder cancer, and prospectively collected at the time of cancer diagnosis, from 01 February 2022 to 01 February 2027

You may qualify if:

• 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.

You may not qualify if:

• 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)
• 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.

Sponsors & Collaborators

• Centre Hospitalier Universitaire, Amiens: lead

Study Sites (1)

Amiens University Hospital

Amiens, 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 45; saint.fabien@chu-amiens.fr

Study Design

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

Study Record Dates

• First Submitted: June 9, 2022
• First Posted: June 13, 2022
• Study Start: May 13, 2022
• Primary Completion (Estimated): May 1, 2027
• Study Completion (Estimated): May 1, 2029
• Last Updated: July 15, 2022

Record last verified: 2022-05

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)