Predicting Outcome of Cytoreduction in Advanced Ovarian Cancer

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест 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

Первичные конечные точки

• a) Number of Participants with Treatment Diagnostic Laparoscopy assessed by Predictive Index Value. [Срок оценки: 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 [Срок оценки: through study completion, an average of 1 year]

Вторичные конечные точки (1)

• 1. Number of Participants with treatment Diagnostic Laparoscopy assessed the images and videos by validating and/or updating an ML model. [Срок оценки: through study completion, an average of 1 year]

Критерии участия

Критерии включения

• 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

Критерии исключения

• 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)

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Диагностика

Центры проведения

Италия · 1 центр

• Fondazione Policlinico Universitario A. Gemelli IRCCS, UOC Ginecologia Oncologica — Roma

Идентификаторы

NCT: NCT06017557 · 6854

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗