Augmented Bladder Tumor Detection Using Real Time Based Artificial Intelligence

Bladder Cancer Clinical Trial

— Bladder-PAD  

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).

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05415631
• Other study ID #: PI2022_843_0014
• Status: Recruiting
• Start date: May 13, 2022
• Est. completion date: May 2029

Study information

• Verified date: May 2022
• Source: Centre Hospitalier Universitaire, Amiens
• Contact: Fabien SAINT, Pr
• Phone: 03 22 45 59 45
• Email: saint.fabien[@]chu-amiens.fr
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: May 2029
• Est. primary completion date: May 2027
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Bladder Cancer
• Cystoscopy
• Urinary Bladder Neoplasms

Locations

Country	Name	City	State
France	Amiens University Hospital	Amiens

Sponsors (1)

Lead Sponsor	Collaborator
Centre Hospitalier Universitaire, Amiens

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Tumor detection rate of white light cystoscopy		one day
Primary	Tumor detection rate of Bladder-PAD cystoscopy		one day
Primary	Tumor false detection rate of white light cystoscopy		one day
Primary	Tumor false detection rate of Bladder-PAD cystoscopy		one day