Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer
Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer, Using a Machine Learning Algorithm and Patterns of Disease Distribution at Laparoscopy (PREDAtOOR)
Fondazione Policlinico Universitario Agostino Gemelli IRCCS
151 participants
Jan 2, 2023
INTERVENTIONAL
Conditions
Summary
PREDAtOOR is a pilot study and this study aims at improving the selection of the best treatment strategy for patients with advanced ovarian cancer by using Camera Vision (CV) to predict outcomes of cyto reduction at the time of Diagnostic laparoscopy.
Eligibility
Inclusion Criteria4
- 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 Criteria4
- 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)
Interventions
With the introduction of artificial intelligence and machine learning, there is a possibility to create more precise prediction models using images from these diagnostic laparoscopy videos. In particular, it would like to use images from the diagnostic laparoscopy to create machine-learning models to help predict if the tumors can be successfully taken out at primary surgery, or if chemotherapy before surgery would be needed. During surgery time the surgical team takes images however, what makes this different is that these images will be used to help create an algorithm to predict surgical outcomes. These images will be stored in a secure database with an anonymous number not linking these pictures to any of the participants.
Locations(1)
View Full Details on ClinicalTrials.gov
For the most up-to-date information, visit the official listing.
NCT06017557