NCT03857373

Brief 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.

Trial Health

77
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
5,000

participants targeted

Target at P75+ for all trials

Timeline
6mo left

Started Feb 2019

Longer than P75 for all trials

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Progress93%
Feb 2019Jan 2027

Study Start

First participant enrolled

February 1, 2019

Completed
25 days until next milestone

First Submitted

Initial submission to the registry

February 26, 2019

Completed
2 days until next milestone

First Posted

Study publicly available on registry

February 28, 2019

Completed
5.8 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

January 1, 2025

Completed
2 years until next milestone

Study Completion

Last participant's last visit for all outcomes

January 1, 2027

Expected
Last Updated

January 30, 2024

Status Verified

January 1, 2024

Enrollment Period

5.9 years

First QC Date

February 26, 2019

Last Update Submit

January 27, 2024

Conditions

Kidney Cancer

Keywords

Renal CancerMachine Learning

Outcome Measures

Primary Outcomes (1)

  • Predicting recurrences

    Predicting recurrences of RCC

    5 years

Study Arms (1)

Renal Cancer

Patients identified with RCC

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodProbability Sample
Study Population

Patients with RCC

You may qualify if:

  • All patient with RCC, who underwent surgery

You may not qualify if:

  • Patients with RCC, who did not underwent surgery

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Zealand University Hospital

Roskilde, 4000, Denmark

RECRUITING

MeSH Terms

Conditions

Kidney Neoplasms

Condition Hierarchy (Ancestors)

Urologic NeoplasmsUrogenital NeoplasmsNeoplasms by SiteNeoplasmsFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesKidney DiseasesUrologic DiseasesMale Urogenital Diseases

Central Study Contacts

Nessn Azawi, Phd

CONTACT

004526393034nesa@regionsjaelland.dk

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Target Duration
5 Years
Sponsor Type
OTHER
Responsible Party
SPONSOR INVESTIGATOR
PI Title
Associate professor

Study Record Dates

First Submitted

February 26, 2019

First Posted

February 28, 2019

Study Start

February 1, 2019

Primary Completion

January 1, 2025

Study Completion (Estimated)

January 1, 2027

Last Updated

January 30, 2024

Record last verified: 2024-01

Data Sharing

IPD Sharing
Will not share

Locations

DK(1)