Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer, Using a Machine Learning Algorithm and Patterns of Disease Distribution At Laparoscopy (PREDAtOOR)

Trial Title: Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer, Using a Machine Learning Algorithm and Patterns of Disease Distribution At Laparoscopy (PREDAtOOR)

NCT ID: NCT06017557

Condition: Ovarian Cancer Stage III
Ovarian Cancer Stage IV

Conditions: Official terms:
Ovarian Neoplasms
Carcinoma, Ovarian Epithelial

Study type: Interventional

Study phase: N/A

Overall status: Not yet recruiting

Study design:

Allocation: N/A

Intervention model: Single Group Assignment

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

Masking: None (Open Label)

Intervention:

Intervention type: Diagnostic Test
Intervention name: Artificial Intelligence
Description: 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.
Arm group label: Clinical Stage III-IV Ovarian Cancer

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.

Criteria for 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)

Gender: Female

Gender based: Yes

Gender description: individuals with a primary diagnosis of suspected Stage III-IV ovarian cancer

Minimum age: 18 Years

Maximum age: N/A

Healthy volunteers: No

Start date: October 29, 2024

Completion date: October 25, 2025

Lead sponsor:
Agency: University Health Network, Toronto
Agency class: Other

Source: University Health Network, Toronto

Record processing date: ClinicalTrials.gov processed this data on November 12, 2024

Source: ClinicalTrials.gov page: https://clinicaltrials.gov/ct2/show/NCT06017557