Renal Cancer Detection Using Convolutional Neural Networks (RCCCNN)

Nessn Azawi

Status: Enrolling

Conditions: Kidney Cancer

Study type: Observational

Funder types: Other

Identifiers

NCT03857373

Zealand_UCRU

Details and patient eligibility

About

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.

Full 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.

Enrollment: 5,000 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

• All patient with RCC, who underwent surgery

Exclusion criteria

• Patients with RCC, who did not underwent surgery

Trial design

5,000 participants in 1 patient group

Renal Cancer

Description:

Patients identified with RCC