AI Reading of CT Scans to Sort Ovarian Tumours Before Surgery
Tests an AI program that sorts ovarian tumours on past CT scans.
Plain-language title generated from the registry record
Registry title: AI Analysis of CT Scans to Classify Ovarian Tumours as Benign, Borderline or Malignant Before Surgery
Key facts
- Where
- Amsterdam, Breda, Eindhoven, Leiden, and Maastricht, NetherlandsSee the 6 sites
Where in Netherlands
- Amsterdam University Medical CentreAmsterdam, NetherlandsNo longer taking part
- Netherlands Cancer Institute-Antoni van LeeuwenhoekAmsterdam, NetherlandsNo longer taking part
- Amphia ZiekenhuisBreda, NetherlandsNo longer taking part
- Catharina ZiekenhuisEindhoven, NetherlandsNo longer taking part
- Leiden University Medical CentreLeiden, NetherlandsNo longer taking part
- Maastricht UMCMaastricht, NetherlandsNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Catharina Ziekenhuis Eindhoven
- Trial code
- NCT07883551
- Record updated
- 20 October 2026
- Original description
The goal of this observational study is to learn if a computer program can tell what type of ovarian tumour a person has from a CT scan taken before surgery. The program uses artificial intelligence (AI). It sorts tumours into three groups:Read full original descriptionShow less
Foundation Model-based AI for Preoperative Three-class Ovarian Tumour Classification: Using Computer Aided Diagnosis (CADx) to Predict Whether an Ovarian Tumour is Benign, Malignant or Borderline
The goal of this observational study is to learn if a computer program can tell what type of ovarian tumour a person has from a CT scan taken before surgery. The program uses artificial intelligence (AI). It sorts tumours into three groups:
* benign: not cancer * borderline: abnormal cells that usually behave less aggressively than cancer * malignant: cancer
Before surgery, doctors try to judge which group a tumour belongs to. This guides where and how the operation is done. This judgement is often hard, especially for borderline tumours. An AI program that does it well could help doctors plan surgery.
The main questions it aims to answer are:
* How well does the AI program sort ovarian tumours into benign, borderline and malignant on CT scans? * Does the AI program work as well on scans from hospitals it did not learn from?
Researchers will compare the AI program's results with the final diagnosis from the tumour tissue removed during surgery. They will also compare the program with methods doctors use today, such as ultrasound-based risk scores and the radiologist's report. In a smaller group, researchers will test the program on MRI scans.
This study only uses information that already exists. It includes adults who had surgery for a mass in or near the ovary at hospitals in the Netherlands and Belgium. Researchers will use:
* CT or MRI scans made before surgery as part of regular care * information from medical records * the tissue diagnosis after surgery
Participants will not be contacted. They will not have any extra scans, tests or visits. Each hospital codes the data before sharing them, so researchers cannot see participants' names. The study does not change anyone's care.
Inclusion: Age 18 years or older at the time of imaging; Ovarian or adnexal mass on clinical and/or radiological assessment that can be reliably identified as the index lesion on CT and/or MRI; Preoperative CT and/or MRI scan available, acquired as part of routine clinical care; Surgical removal of the ovarian or adnexal mass, with definitive postoperative histopathology available as reference standard; Bilateral ovarian tumours are eligible. If histopathology differs between sides, a predefined rule is applied (for example, selection of the lesion with the highest malignant potential); Advanced ovarian malignancy, including ascites or peritoneal disease, is eligible if the primary adnexal mass can be clearly identified on imaging.
Exclusion: Previous ovarian malignancy or prior oncological treatment affecting the adnexal region; Extra-ovarian disease that prevents reliable identification or attribution of the adnexal lesion as the index tumour (for example diffuse peritoneal carcinomatosis or a confirmed non-ovarian primary malignancy); No definitive postoperative histopathology available; Imaging of insufficient diagnostic quality (for example incomplete series, corrupted files or non-diagnostic sequences); Pathology report judged indefinite or inconclusive by the reviewing clinician.