Renal Cancer Detection Using Convolutional Neural Networks — RCCCNN  

Kidney Cancer Clinical Trial

Official title: Renal Cancer Detection Using Convolutional Neural Networks

Status Recruiting

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.

Clinical Trial 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. ;

Related Conditions & MeSH terms

• Carcinoma, Renal Cell
• Kidney Cancer
• Kidney Neoplasms

• NCT number: NCT03857373
• Study type: Observational [Patient Registry]
• Source: Zealand University Hospital
• Contact: Nessn Azawi, Phd
• Phone: 004526393034
• Email: nesa@regionsjaelland.dk
• Status: Recruiting
• Start date: February 1, 2019
• Completion date: January 1, 2027