Renal Cancer Detection Using Convolutional Neural Networks

Recruiting

• Conditions: Kidney Cancer

• Registration Number: NCT03857373
• Lead Sponsor: Nessn Azawi

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.

Study Timeline

• Study Start: 2019-02-01
• Study First Submitted: 2019-02-26
• Study First Submitted QC: 2019-02-26
• Study First Posted: 2019-02-28
• Status Verified: 2024-01
• Last Update Submitted: 2024-01-27
• Last Update Posted: 2024-01-30
• Primary Completion: 2025-01-01
• Study Completion: 2027-01-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 5000

Inclusion Criteria

• All patient with RCC, who underwent surgery

Exclusion Criteria

• Patients with RCC, who did not underwent surgery

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Predicting recurrences	5 years	Predicting recurrences of RCC

Trial Locations

Locations (1)

Zealand University Hospital; 🇩🇰 Roskilde, Denmark