Clinical trial NCT05415631: Augmented Bladder Tumor Detection Using Real Time Based Artificial Intelligence
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).
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Bladder Cancer
Cystoscopy
Artificial Intelligence
Keywords: Bladder Cancer
cystoscopy
Artificial Intelligence
Lead sponsor: Centre Hospitalier Universitaire, Amiens
Sponsor class: OTHER
First posted: 2022-06-13
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
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

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.


Eligibility criteria: 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.

Primary outcomes:
measure: Tumor detection rate of white light cystoscopy
timeFrame: one day
measure: Tumor detection rate of Bladder-PAD cystoscopy
timeFrame: one day
measure: Tumor false detection rate of white light cystoscopy
timeFrame: one day
measure: Tumor false detection rate of Bladder-PAD cystoscopy
timeFrame: one day
Secondary outcomes:
Other outcomes:
Locations:
city: Amiens
country: France
facility: Amiens University Hospital
Study officials:
References: