Bladder Cancer Detection Using Convolutional Neural Networks (BLAInostic)

January 28, 2024 updated by: Zealand University Hospital
The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

The investigators aim to experiment and implement various deep learning architectures to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, the investigators are interested in detecting bladder tumors from CT urography scans and cystoscopies of the bladder in this project. The investigators want to classify bladder tumors as cancer, non cancer, high grade and low grade, invasive and non-invasive, with high sensitivity and low false positive rate using various convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for bladder cancer diagnosis. Moreover, by automating this task, the investigator scan significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans and reduce the false-negative and positive that can happen due to human evaluation cystoscopies.

Study Type

Observational

Enrollment (Estimated)

5000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Denmark
      • Roskilde, Denmark, 4000
        • Recruiting
        • Zealand University Hospital
        • Contact:
        • Principal Investigator:
          • Nessn Azawi, Ph.D

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Patients with micro or macroscopic hematuria

Description

Inclusion Criteria:

  • Patients with first time hematuria
  • Patients with the control program for previous bladder cancer

Exclusion Criteria:

  • Patients with control cystoscope for noncancer suspected disease

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Detecting bladder tumor
Patients with hematuria, or previous bladder tumor
Diagnostic test: Al_bladder
Detection of bladder tumor with help of Artificial intelligence

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparing standard technique to Machine Learning
Time Frame: 5 years
The accuracy of Machine learning to detect bladder cancer compared to standard cystoscopy
5 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Detecting accuracy of subtypes of bladder cancer
Time Frame: 5 years
The abelity of Machine Learning to identify high grad bladder cancer from low grad bladder cancer
5 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Zealand University Hospital

Investigators

  • Principal Investigator: Nessn Azawi, phd, Zealand University Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

June 1, 2021

Primary Completion (Estimated)

June 1, 2026

Study Completion (Estimated)

June 1, 2026

Study Registration Dates

First Submitted

November 17, 2021

First Submitted That Met QC Criteria

January 14, 2022

First Posted (Actual)

January 18, 2022

Study Record Updates

Last Update Posted (Actual)

January 30, 2024

Last Update Submitted That Met QC Criteria

January 28, 2024

Last Verified

January 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SJ-905

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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