Clinical Research Directory

AI Analysis of CT Scans to Classify Ovarian Tumours as Benign, Borderline or Malignant Before Surgery

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.

Assessment of Compliance With Monitoring Conducted by a Physician in Person or by a Nurse in Remote Monitoring

This is a multicenter, interventional, randomized study among adult patients recently diagnosed with a rare tumor (\<12 months). The study will aim to compare compliance with the personalized post-treatment surveillance plan, established for each patient according to national guidelines, when the surveillance is conducted in person by a hospital-based physician (control arm) or remotely by a trained nurse (experimental arm).