CADx - Radiomics to Distinguish the Origin of Ovarian Tumors

NCT ID: NCT05174377

Last Updated: 2022-01-25

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

600 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-04-05

Study Completion Date

2025-08-01

Brief Summary

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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.

Detailed Description

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This research focuses on improving the accuracy of the determination of the nature (benign or malignant) of ovarian tumors by making use of artificial intelligence by creating a CT-scan algorithm. This because a correct preoperative classification of ovarian tumors is essential for appropriate treatment. Existing prediction models often lead to unnecessary referrals to gynecological oncology hospitals, resulting in higher costs and increased stress for the patient. It is therefore important to evaluate other strategies to differentiate between benign and malignant ovarian tumors.

Artificial Intelligence (AI) for radiology is currently being developed by the Eindhoven University of Technology (TU/e) and Philips Research Europe and may provide a potential solution to this problem.

The currently developed algorithm (CADx), using a support vector machine (SVM), showed within a small population of about 100 patients a sensitivity of 74% and specificity of 74%. These are promising results to train this algorithm even further with more CT-scans images and the addition of clinical variables and even liquid biopsies.

Type of study: Retrospective study cohort This is a retrospective analysis on known data in which definitive patients diagnosis has already been established and current analysis will not affect treatment plan.

No products for patients are used, only computer aided diagnosis is used on existing radiological imaging, namely CT-scans.

This study is linked to two other Dutch trials in which ovarian tumor biomarkers are assessed in order to find out the origin of ovarian tumors preoperatively.

The first is the HE4-prediction study, with local protocol ID NL58253.031.16. The second is the OVI-DETECT study, with clinicaltrial.gov number NCT04971421.

Conditions

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Ovarian Cancer

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Interventions

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CT-scan algorithm

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

Intervention Type DIAGNOSTIC_TEST

Other Intervention Names

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Support vector machine

Eligibility Criteria

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Inclusion Criteria

* 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

Exclusion Criteria

* indefinite pathology report
* lack of correct description of staging in OR report when applicable
Minimum Eligible Age

18 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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The Netherlands Cancer Institute

OTHER

Sponsor Role collaborator

Eindhoven University of Technology

OTHER

Sponsor Role collaborator

Amsterdam UMC, location VUmc

OTHER

Sponsor Role collaborator

Leiden University Medical Center

OTHER

Sponsor Role collaborator

Amphia Hospital

OTHER

Sponsor Role collaborator

Gynaecologisch Oncologisch Centrum Zuid

OTHER

Sponsor Role lead

Responsible Party

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Jurgen M.J. Piek

MD-PhD

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Catharina hospital

Eindhoven, North Brabant, Netherlands

Site Status RECRUITING

Netherlands Cancer Institute

Amsterdam, North Holland, Netherlands

Site Status RECRUITING

Leiden University Medical Center

Leiden, North Holland, Netherlands

Site Status RECRUITING

Amsterdam medical center

Amsterdam, , Netherlands

Site Status NOT_YET_RECRUITING

Amphia hospital

Breda, , Netherlands

Site Status RECRUITING

Countries

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Netherlands

Central Contacts

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Jurgen Piek, MD-PhD

Role: CONTACT

040 - 239 91 11

Anna Koch, MD

Role: CONTACT

020-512 4303

Facility Contacts

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Jurgen M Piek, MD. PhD.

Role: primary

+31(0)40 239 9111

Christianne Lok, MD; PhD

Role: primary

020 512 2957

Cor D de Kroon, MD, PhD

Role: primary

071-5299288

Stijn Mom, MD-PhD

Role: primary

020-7328300

Janneke Hoogstad, MD-PhD

Role: primary

(076) 595 10 03

Related Links

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Other Identifiers

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RADIOMICS

Identifier Type: -

Identifier Source: org_study_id

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