Using Computer Software to Detect Kidney Cancer on CT Scans
Tests computer programs that read kidney CT scans to tell tumors and cysts apart.
Plain-language title generated from the registry record
Registry title: Renal Cancer Detection Using Convolutional Neural Networks
Key facts
- Where
- Roskilde, DenmarkSee the site
Where in Denmark
- Zealand University HospitalRoskilde, DenmarkTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Nessn Azawi
- Trial code
- NCT03857373
- Record updated
- 30 January 2024
- Original 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.Read full original descriptionShow less
Renal Cancer Detection Using Convolutional Neural Networks
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.
Inclusion: All patient with RCC, who underwent surgery.
Exclusion: Patients with RCC, who did not underwent surgery.