Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer

NCT06017557 | Recruiting

• 1 other identifier: 6854
• Study Type: interventional
• Target: 151
• Locations: 1 country
• Sites: 1

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.

Conditions

Ovarian Cancer Stage III; Ovarian Cancer Stage IV

Outcome Measures

Primary Outcomes (2)

• a) Number of Participants with Treatment Diagnostic Laparoscopy assessed by Predictive Index Value.

  The Fagotti score, also known as the Predictive Index Value (PIV), is determined through the evaluation of six abdominal areas during laparoscopic exploration. These areas include the parietal peritoneum, diaphragm, greater omentum, bowel, stomach/spleen/lesser omentum, and liver. A score of 2 is assigned to each area with visible tumor spread, allowing for a maximum score of 14. Notably, a PIV score of 10 or higher signifies a threshold for triaging patients toward neoadjuvant chemotherapy. To create a predictive model for cytoreduction outcomes during diagnostic laparoscopy, advanced deep neural networks will be trained. This aims to automate PIV score assessment using a fully supervised approach and deduce features from images obtained during diagnostic laparoscopy to predict the possibility of a resection target above 1 cm or a lack of indication for cytoreductive surgery in a weekly supervised manner.

  through study completion, an average of 1 year

• b)Number of Participants with Treatment Diagnostic Laparoscopy assessed by utilizing machine learning and computer vision models to analyze images and videos

  The laparoscopic evaluation also demonstrated its efficacy in foreseeing surgical outcomes for patients undergoing interval cytoreductive surgery post neoadjuvant chemotherapy (NACT). However, this model remains vulnerable to the subjectivity inherent in each surgeon's evaluation of individual disease sites. Evaluating patients during intraoperative procedures during diagnostic laparoscopy often relies on a surgeon's judgment, which may not always be optimally trained for such evaluations and can be influenced by biases. Utilizing CV models can involve training them to automatically replicate expert assessments, providing more accurate evaluations for a larger patient population.

  through study completion, an average of 1 year

Secondary Outcomes (1)

• 1. Number of Participants with treatment Diagnostic Laparoscopy assessed the images and videos by validating and/or updating an ML model.

  through study completion, an average of 1 year

Study Arms (1)

Clinical Stage III-IV Ovarian Cancer

EXPERIMENTAL

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.

Diagnostic Test: Artificial Intelligence

Interventions

Artificial Intelligence 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.

Clinical Stage III-IV Ovarian Cancer

Eligibility Criteria

• Age: 18 Years+
• Gender Eligibility Details: individuals with a primary diagnosis of suspected Stage III-IV ovarian cancer
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• Fondazione Policlinico Universitario Agostino Gemelli IRCCS: lead

Study Sites (1)

Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Ginecologia Oncologica

Roma, 00168, Italy

RECRUITING

MeSH Terms

Conditions

Ovarian Neoplasms

Interventions

Artificial Intelligence

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Ovarian Diseases; Adnexal Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Neoplasms, Female; Urogenital Neoplasms; Genital Diseases; Endocrine System Diseases; Gonadal Disorders

Intervention Hierarchy (Ancestors)

Algorithms; Mathematical Concepts

Study Officials

• Anna Fagotti, Prof

  Fondazione Policlinico Universitario A. Gemelli, IRCCS

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Liat Hogen, MD

CONTACT

416-946-4501; liat.hogen@uhn.ca

Ferdous Parveen, MBBS

CONTACT

416-946-4501; ferdous.parveen@uhn.ca

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: 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

Study Record Dates

• First Submitted: August 10, 2023
• First Posted: August 30, 2023
• Study Start: January 2, 2023
• Primary Completion: December 15, 2025
• Study Completion (Estimated): September 1, 2026
• Last Updated: December 19, 2025

Record last verified: 2025-12

Locations

IT (1)