Renal Cancer Detection Using Convolutional Neural Networks

Kidney Cancer Clinical Trial

— RCCCNN  

Official title:

Renal Cancer Detection Using Convolutional Neural Networks

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03857373
• Other study ID #: Zealand_UCRU
• Status: Recruiting
• Start date: February 1, 2019
• Est. completion date: January 1, 2027

Study information

• Verified date: January 2024
• Source: Zealand University Hospital
• Contact: Nessn Azawi, Phd
• Phone: 004526393034
• Email: nesa[@]regionsjaelland.dk
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 5000
• Est. completion date: January 1, 2027
• Est. primary completion date: January 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• All patient with RCC, who underwent surgery

Exclusion Criteria:

• Patients with RCC, who did not underwent surgery

Related Conditions & MeSH terms

• Carcinoma, Renal Cell
• Kidney Cancer
• Kidney Neoplasms

Locations

Country	Name	City	State
Denmark	Zealand University Hospital	Roskilde

Sponsors (1)

Lead Sponsor	Collaborator
Nessn Azawi

Country where clinical trial is conducted

Denmark, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Predicting recurrences	Predicting recurrences of RCC	5 years