Using laparoscopy camera images to predict whether surgery can remove advanced ovarian cancer
Official title: Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer, Using a Machine Learning Algorithm and Patterns of Disease Distribution at Laparoscopy (PREDAtOOR)
Run by Fondazione Policlinico Universitario Agostino Gemelli IRCCS · NCT06017557 · Data updated 2026-11-12
This project will try to use videos taken during diagnostic laparoscopy to predict whether surgeons can remove all visible tumors at primary surgery for people with stage III–IV ovarian cancer.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years and up |
| Sex | Women only |
| Enrollment | 151 (estimated) |
| Sponsor | Fondazione Policlinico Universitario Agostino Gemelli IRCCS Academic / other |
| Drugs / interventions | chemotherapy |
| Locations | 1 site (Roma) |
| Status | Recruiting |
| Started | January 2, 2023 |
| Main results expected | December 15, 2025 |
| Study ends | September 1, 2026 |
| Registry record updated | December 19, 2025 |
| Trial ID | NCT06017557 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
People with suspected stage III–IV ovarian cancer who are fit for cytoreductive surgery and undergoing diagnostic laparoscopy at a participating center are ideal candidates.
✕Probably not a fit
Patients with early-stage (I–II) disease, those unfit for surgery, or those with certain histologic subtypes (low‑grade serous, clear cell, mucinous, or non‑epithelial) are unlikely to benefit from the model's predictions.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
PREDAtOOR is a pilot interventional effort that collects images from diagnostic laparoscopy to train machine-learning models linking intra‑abdominal appearance to surgical outcomes. Patients are enrolled at the time of their planned surgery or interval cytoreduction, with no extra visits beyond standard care. Collected laparoscopic images and clinical data will be used to develop and test algorithms that predict the likelihood of complete cytoreduction. Outcomes from the subsequent primary or interval surgery will be compared with model predictions to estimate accuracy and refine the approach.
Why it matters
Potential benefit: If successful, this approach could help doctors choose the best first treatment—surgery or chemotherapy—so patients avoid unnecessary operations and receive the therapy most likely to remove their tumors.
How similar studies have gone: CT/MRI and laparoscopy scoring systems have been used to predict resectability and early AI work in surgical imaging is promising, but applying machine learning to laparoscopy video specifically for ovarian cytoreduction prediction remains experimental.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients treated at Fondazione Policlinico Gemelli Hospital, Rome Italy, Trillium -Credit Valley Hospital, Mississauga, Ontario and Princess Margaret Cancer Centre, Toronto, Canada * Patients fit for cytoreductive surgery * Patients with a primary diagnosis of suspect Stage III-IV ovarian cancer * Patients selected for interval cytoreductive surgery after NACT Exclusion Criteria: * Patients with pre-operative Stage I-II disease confined to the pelvis * Patients unfit for surgery * Lack of information about patients' surgical outcomes and clinicopathological characteristics * LGSOC, Clear cell and mucinous, non-epithelial histologic subtypes (if available)
Where it's running
-
Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Ginecologia Oncologica
Roma, Italy Recruiting
Study contacts
- Principal investigator: Anna Fagotti, Prof — Fondazione Policlinico Universitario Agostino Gemelli IRCCS
- Study coordinator: Liat Hogen, MD
- Email: liat.hogen@uhn.ca
- Phone: 416-946-4501
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.