An observational study in Ovarian Cancer, sponsored by Gynaecologisch Oncologisch Centrum Zuid. Recruiting at 5 sites in Netherlands. Open to female participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2022-01-25.
Sponsored by Gynaecologisch Oncologisch Centrum Zuid · Observational
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.
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.
3,381 studies on the registry are indexed under Ovarian Neoplasms; 790 are open to participants now.
This study's planned enrollment of 600 is above the median of 200 across 616 observational studies indexed under Ovarian Neoplasms.
Browse Ovarian Neoplasms studies →Gynaecologisch Oncologisch Centrum Zuid is the lead sponsor of 6 studies on the registry; 3 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients with an ovarian tumor of which it is unknown whether it is benign or malignant referred for staging laparotomy.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Cancer (malignancy)
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
CADx model was developed with a Support Vector Machine (SVM) algorithm and trained using five-fold cross-validation
Also known as: Support vector machine
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
Time frame: 3 - 4 years
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
Time frame: 3 - 4 years
Plan to share: Undecided
Started Apr 2021 (actual). Primary completion is estimated for Aug 2024, 39 months after the start.
How these figures are worked out, and which studies are left out of them.
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Gynaecologisch Oncologisch Centrum Zuid