Augmented Bladder Tumor Detection Using Real Time Based Artificial Intelligence
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Bladder Cancer, Cystoscopy, Artificial Intelligence. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- France
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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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).
Overview
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.
Primary outcome measures
- Tumor detection rate of white light cystoscopy [Time frame: one day]
- Tumor detection rate of Bladder-PAD cystoscopy [Time frame: one day]
- Tumor false detection rate of white light cystoscopy [Time frame: one day]
- Tumor false detection rate of Bladder-PAD cystoscopy [Time frame: one day]
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-only
Study locations
France · 1 center
- Amiens University Hospital — Amiens
Identifiers
NCT: NCT05415631 · PI2022_843_0014