NCT05174377

Brief Summary

In women with an ovarian tumor, it is often unclear whether the tumor is benign or malignant. To differentiate, tumor markers (CA125 and CEA), a transvaginal ultrasound and, depending on the ultrasound image and the CA125 concentration, a CT scan are performed. The quality of radiological imaging in diagnosing abdominal pathology is often not accurate enough, making additional interventions no-dig for proper classification and interpretation of the tumor. Objective: To improve accuracy for distinguishing benign from malignant disease in patients presenting with an ovarian mass by using a computer aided detection algorithm.

Trial Health

43
At Risk

Trial Health Score

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

Trial has exceeded expected completion date
Enrollment
600

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Apr 2021

Longer than P75 for all trials

Geographic Reach
1 country

5 active sites

Status
unknown

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 Start

First participant enrolled

April 5, 2021

Completed
8 months until next milestone

First Submitted

Initial submission to the registry

December 13, 2021

Completed
17 days until next milestone

First Posted

Study publicly available on registry

December 30, 2021

Completed
2.6 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

August 1, 2024

Completed
1 year until next milestone

Study Completion

Last participant's last visit for all outcomes

August 1, 2025

Completed
Last Updated

January 25, 2022

Status Verified

January 1, 2022

Enrollment Period

3.3 years

First QC Date

December 13, 2021

Last Update Submit

January 10, 2022

Conditions

Outcome Measures

Primary Outcomes (1)

  • Sensitivity and specificity of CADx algorithm

    Percentage of correct determination of malignancy by the Risk of Malignancy Index (RMI) compared to exact determination by CAD assessment in patients with an ovarian tumor

    3 - 4 years

Secondary Outcomes (1)

  • Sensitivity and specificity of CADx algorithm with additional variables

    3 - 4 years

Interventions

CT-scan algorithmDIAGNOSTIC_TEST

CADx model was developed with a Support Vector Machine (SVM) algorithm and trained using five-fold cross-validation

Also known as: Support vector machine

Eligibility Criteria

Age18 Years+
Sexfemale
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients with an ovarian tumor of which it is unknown whether it is benign or malignant referred for staging laparotomy.

You may qualify if:

  • patients with an ovarian tumor of which it is unknown whether it is benign or malignant (Risk of Malignancy Index (RMI) \>200)
  • underwent surgery
  • histological proof of tumor

You may not qualify if:

  • indefinite pathology report
  • lack of correct description of staging in OR report when applicable

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (5)

Catharina hospital

Eindhoven, North Brabant, 5623EJ, Netherlands

RECRUITING

Netherlands Cancer Institute

Amsterdam, North Holland, 1066 CX, Netherlands

RECRUITING

Leiden University Medical Center

Leiden, North Holland, 2333 ZA, Netherlands

RECRUITING

Amsterdam medical center

Amsterdam, Netherlands

NOT YET RECRUITING

Amphia hospital

Breda, Netherlands

RECRUITING

Related Links

Biospecimen

Retention: SAMPLES WITH DNA

blood based liquid biopsies such ctDNA and tumor DNA.

MeSH Terms

Conditions

Ovarian Neoplasms

Interventions

Support Vector Machine

Condition Hierarchy (Ancestors)

Endocrine Gland NeoplasmsNeoplasms by SiteNeoplasmsOvarian DiseasesAdnexal DiseasesGenital Diseases, FemaleFemale Urogenital DiseasesFemale Urogenital Diseases and Pregnancy ComplicationsUrogenital DiseasesGenital Neoplasms, FemaleUrogenital NeoplasmsGenital DiseasesEndocrine System DiseasesGonadal Disorders

Intervention Hierarchy (Ancestors)

Supervised Machine LearningMachine LearningArtificial IntelligenceAlgorithmsMathematical ConceptsClassification Algorithms

Central Study Contacts

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
MD-PhD

Study Record Dates

First Submitted

December 13, 2021

First Posted

December 30, 2021

Study Start

April 5, 2021

Primary Completion

August 1, 2024

Study Completion

August 1, 2025

Last Updated

January 25, 2022

Record last verified: 2022-01

Locations