Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer, Using a Machine Learning Algorithm and Patterns of Disease Distribution at Laparoscopy (PREDAtOOR) (PREDAtOOR)
Ovarian Cancer Stage III, Ovarian Cancer Stage IV
About this trial
This is an interventional diagnostic trial for Ovarian Cancer Stage III
Eligibility 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)
Sites / Locations
Arms of the Study
Arm 1
Experimental
Clinical Stage III-IV Ovarian Cancer
individuals who have been diagnosed or are suspected to have Clinical Stage III-IV Ovarian Cancer and CT and MRI have most commonly been used to identify sites and amounts of tumors in the abdomen and can help determine if these tumors can be safely removed by surgery. However, these imaging methods are only a prediction, and sometimes a diagnostic laparoscopy (putting a camera in the abdomen to look at all sites of disease) is performed to help this decision process.