RecruitingNot ApplicableNCT06017557

Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer

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

Sponsor

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Enrollment

151 participants

Start Date

Jan 2, 2023

Study Type

INTERVENTIONAL

Conditions

Ovarian Cancer Stage III Ovarian Cancer Stage IV

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.

Eligibility

Sex: FEMALEMin Age: 18 Years

Plain Language Summary

Simplified for easier understanding

This study aims to develop better tools to predict whether surgery (called cytoreduction) will successfully remove all visible cancer in women with advanced ovarian cancer, helping surgeons make better treatment decisions. **You may be eligible if...** - You are being treated for suspected Stage III or IV ovarian cancer - You are fit for surgery - You have been selected for surgery after initial chemotherapy (interval cytoreduction) - You are being treated at one of the participating hospitals in Italy or Canada **You may NOT be eligible if...** - Your cancer is early-stage (Stage I–II) and confined to the pelvis - You are not fit for surgery - There is not enough information about your surgical outcomes or medical history available - You have certain uncommon subtypes of ovarian cancer (e.g., clear cell, mucinous, or non-epithelial) Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

DIAGNOSTIC_TESTArtificial 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.

Locations(1)

Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Ginecologia Oncologica

Roma, Italy

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NCT06017557

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