Clinical trial NCT06017557: Predicting Outcome of Cytoreduction in 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)
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Ovarian Cancer Stage III
Ovarian Cancer Stage IV
Lead sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Sponsor class: OTHER
First posted: 2023-08-30
Enrollment: 50
Sex: FEMALE
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: This study focuses on individuals diagnosed or suspected to have Stage III-IV ovarian cancer They must be fit for cytoreductive surgery These individuals also be selected for interval cytoreductive surgery after NACT
Primary purpose: DIAGNOSTIC

Brief 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.

Detailed description: For the treatment of advanced ovarian cancer, the decision to undergo primary surgery is complex and decided by the surgeon while multiple considering multiple elements. Sometimes, chemotherapy is needed before surgery to shrink some of the tumours. To choose the best patients for primary surgery, several prediction tools have been developed. CT and MRI have most commonly been used to identify sites and amounts of tumors in the abdomen and can help determine if these tumours 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.

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, the investigators would like to use images from the diagnostic laparoscopy to create machine-learning models to help predict if the tumours can be successfully taken out at primary surgery, or if chemotherapy before surgery would be needed.

The investigators will enroll patients at a one-time point (being the time of surgery) and follow them forward in time and There will be no additional visits other than the surgery.

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.

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)

Primary outcomes:
measure: a) Number of Participants with Treatment Diagnostic Laparoscopy assessed by Predictive Index Value.
timeFrame: through study completion, an average of 1 year
measure: b)Number of Participants with Treatment Diagnostic Laparoscopy assessed by utilizing machine learning and computer vision models to analyze images and videos
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
measure: 1. Number of Participants with treatment Diagnostic Laparoscopy assessed the images and videos by validating and/or updating an ML model.
timeFrame: through study completion, an average of 1 year
Other outcomes:
Locations:
city: Roma
country: Italy
facility: Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Ginecologia Oncologica
Study officials:
affiliation: Fondazione Policlinico Universitario A. Gemelli, IRCCS
name: Anna Fagotti, Prof
role: PRINCIPAL_INVESTIGATOR
References: