Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer

NCT ID: NCT06017557

Last Updated: 2025-12-19

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 151 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-01-02
• Study Completion Date: 2026-09-01

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.

Conditions

Ovarian Cancer Stage III; Ovarian Cancer Stage IV

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

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.

Group Type: EXPERIMENTAL

Artificial Intelligence

Intervention Type: DIAGNOSTIC_TEST

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.

Interventions

Artificial Intelligence

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.

Intervention Type: DIAGNOSTIC_TEST

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)

• Minimum Eligible Age: 18 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Anna Fagotti, Prof

Role: PRINCIPAL_INVESTIGATOR

Fondazione Policlinico Universitario A. Gemelli, IRCCS

Locations

Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Ginecologia Oncologica

Roma, , Italy

Site Status: RECRUITING

Countries

Italy

Central Contacts

Liat Hogen, MD

Role: CONTACT

Phone: 416-946-4501

Email: liat.hogen@uhn.ca

Ferdous Parveen, MBBS

Role: CONTACT

Phone: 416-946-4501

Email: ferdous.parveen@uhn.ca

Facility Contacts

Anna Fagotti, Prof

Role: primary

Riccardo Oliva

Role: backup

Other Identifiers

6854

Identifier Type: -

Identifier Source: org_study_id