Ovarian CancerCADxNCT05174377
CADx - Radiomics to Distinguish the Origin of Ovarian Tumors
An observational study of Ovarian Cancer, run by Gynaecologisch Oncologisch Centrum Zuid at 5 sites in Netherlands.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older, women only
- Conditions the study is for
- Ovarian Cancer
- What is being tested?
- CT-scan algorithm
- How many people
- 600 participants
- Where
- 5 sitesNetherlands
- Study dates
- 5 Apr 2021to 1 Aug 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Women only
- Adult, Older adult
Study population: Patients with an ovarian tumor of which it is unknown whether it is benign or malignant referred for staging laparotomy.
Who may be able to join
- 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
Who cannot join
- indefinite pathology report
- lack of correct description of staging in OR report when applicable
Trial locations and contacts
5 sites in 1 country.
5 sites in 1 country
Netherlands5 sites, 5 recruiting
- Recruiting
Netherlands Cancer Institute
Amsterdam, North Holland, 1066 CX
Christianne Lok, MD; PhD, 020 512 2957, c.lok@nki.nl
- Not yet recruiting
Amsterdam medical center
Amsterdam
Stijn Mom, MD-PhD, 020-7328300, c.mom@amsterdamumc.nl
- Recruiting
Amphia hospital
Breda
Janneke Hoogstad, MD-PhD, (076) 595 10 03, JHoogstad@amphia.nl
Dineke Smedts, MD-PhD
- Recruiting
Catharina hospital
Eindhoven, North Brabant, 5623EJ
Jurgen M Piek, MD. PhD., +31(0)40 239 9111, jurgen.piek@catharinaziekenhuis.nl
- Recruiting
Leiden University Medical Center
Leiden, North Holland, 2333 ZA
Cor D de Kroon, MD, PhD, 071-5299288, cordekroon@lumc.nl
About this study
Official title: Computer-aided Radiology for Cancer Detection and Therapy Stratification - Benign or Malignant Ovarian Tumors.
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.
Read the detailed description
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
- Ovarian Cancer
- Being tested
- CT-scan algorithm
- Medical subject headings
- Ovarian Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CT-scan algorithmDiagnostic test
Also called Support vector machine
CADx model was developed with a Support Vector Machine (SVM) algorithm and trained using five-fold cross-validation
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 - 4 years
Secondary outcomes (1)
Sensitivity and specificity of CADx algorithm with additional variables
Correlation of the findings from CAD analysis in some patients with analysis of circulating tumor (ct) DNA and protein tumor markers or other additional clinical variables
Measured: 3 - 4 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Dec 2021
- Study start5 Apr 2021
- Primary completion1 Aug 2024 (estimated)
- Study completion1 Aug 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ovarian CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Gynaecologisch Oncologisch Centrum Zuid
- Sponsor type
- University, hospital or other
- Collaborators
- The Netherlands Cancer Institute, Eindhoven University of Technology, Amsterdam UMC, location VUmc, Leiden University Medical Center, Amphia Hospital
- Responsible party
- Principal investigator, Jurgen M.J. Piek, Gynaecologisch Oncologisch Centrum Zuid
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05174377
- Sponsor's protocol ID
- RADIOMICS
- Acronym
- CADx
- Status last verified
- Jan 2022
- First submitted
- 13 Dec 2021
- Last updated
- 25 Jan 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 25 Jan 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Computer-aided Radiology for Cancer Detection and Therapy Stratification - Benign or Malignant Ovarian Tumors.
- ClinicalTrials.gov ID
- NCT05174377
- Acronym / sponsor ID
- CADx / RADIOMICS
- Conditions
- Ovarian Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Gynaecologisch Oncologisch Centrum ZuidUniversity, hospital or other
- Collaborators
- The Netherlands Cancer Institute, Eindhoven University of Technology, Amsterdam UMC, location VUmc, Leiden University Medical Center, Amphia Hospital
- Study start
- 5 Apr 2021 (actual)
- Primary completion
- 1 Aug 2024 (estimated)
- Study completion
- 1 Aug 2025 (estimated)
- First posted
- 30 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- Female only
- Age groups
- Adult, Older adult
- Study population
- Patients with an ovarian tumor of which it is unknown whether it is benign or malignant referred for staging laparotomy.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (2 listed)
- indefinite pathology report
- lack of correct description of staging in OR report when applicable
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CT-scan algorithmAlso: Support vector machineCADx model was developed with a Support Vector Machine (SVM) algorithm and trained using five-fold cross-validation | Diagnostic test | – |
Primary outcome
Sensitivity and specificity of CADx algorithm
Time frame: 3 - 4 years
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
1 secondary outcome
Sensitivity and specificity of CADx algorithm with additional variables
Time frame: 3 - 4 years
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jan 2022.
- OVI-DETECT study link at clinicaltrial.govLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
5 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ovarian Cancer, run by Gynaecologisch Oncologisch Centrum Zuid at 5 sites in Netherlands.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 25 Jan 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 5 sites in Netherlands. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jurgen Piek, MD-PhD (040 - 239 91 11, jurgen.piek@catharinaziekenhuis.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity and specificity of CADx algorithm (time frame: 3 - 4 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Jan 2022.