Detecting kidney cancer using advanced machine learning techniques
Renal Cancer Detection Using Convolutional Neural Networks
Zealand University Hospital · NCT03857373
This study is testing if advanced computer programs can help doctors more accurately spot kidney cancer in CT scans.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 5000 (estimated) |
| Sex | All |
| Sponsor | Zealand University Hospital (other) |
| Locations | 1 site (Roskilde) |
| Trial ID | NCT03857373 on ClinicalTrials.gov |
What this trial studies
This project focuses on utilizing deep learning architectures, specifically convolutional neural networks (CNN), to enhance the accuracy of computer-aided diagnosis (CAD) systems for renal cancer detection. The study aims to classify renal tumors from CT urography scans into various categories, including cancerous and non-cancerous types, while minimizing false positives. By automating the detection process, the study seeks to significantly reduce the time required for radiologists to create large-scale labeled datasets, ultimately improving diagnostic efficiency and accuracy.
Who should consider this trial
Good fit: Ideal candidates for this study are patients diagnosed with renal cell carcinoma (RCC) who have undergone surgical treatment.
Not a fit: Patients with RCC who have not undergone surgery may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to faster and more accurate diagnoses of kidney cancer, improving patient outcomes.
How similar studies have performed: Other studies utilizing machine learning for cancer detection have shown promising results, indicating potential success for this approach.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * All patient with RCC, who underwent surgery Exclusion Criteria: * Patients with RCC, who did not underwent surgery
Where this trial is running
Roskilde
- Zealand University Hospital — Roskilde, Denmark (RECRUITING)
Study contacts
- Study coordinator: Nessn Azawi, Phd
- Email: nesa@regionsjaelland.dk
- Phone: 004526393034
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Kidney Cancer, Renal Cancer, Machine Learning