Renal Cancer Detection Using Convolutional Neural Networks

NCT ID: NCT03857373

Last Updated: 2024-01-30

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 5000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-02-01
• Study Completion Date: 2027-01-01

Brief Summary

We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.

Detailed Description

We aim to experiment and implement various deep learning architectures in order to achieve human-level accuracy in Computer-aided diagnosis (CAD) systems. In particular, we are interested in detecting renal tumors from CT urography scans in this project. We would like to classify renal tumor to cancer, non cancer, renal cyst I, renal cyst II, renal cyst III and renal cyst VI, with high sensitivity and low false positive rate using various types of convolutional neural networks (CNN). This task can be considered as the first step in building CAD systems for renal cancer diagnosis. Moreover, by automating this task, we can significantly reduce the time for the radiologists to create large-scale labeled datasets of CT-urography scans.

Conditions

Kidney Cancer

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Renal Cancer

Patients identified with RCC

No interventions assigned to this group

Eligibility Criteria

Inclusion Criteria

• All patient with RCC, who underwent surgery

Exclusion Criteria

• Patients with RCC, who did not underwent surgery

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Nessn Azawi

OTHER

Sponsor Role: lead

Responsible Party

Nessn Azawi

Associate professor

Responsibility Role: SPONSOR_INVESTIGATOR

Locations

Zealand University Hospital

Roskilde, , Denmark

Site Status: RECRUITING

Countries

Denmark

Central Contacts

Nessn Azawi, Phd

Role: CONTACT

nesa@regionsjaelland.dk

004526393034

Facility Contacts

Nessn H. Azawi, M.D.

Role: primary

nesa@regionsjaelland.dk

004526393034

Other Identifiers

Zealand_UCRU

Identifier Type: -

Identifier Source: org_study_id