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NCT Number: NCT06331130

Mesenteric Infiltration in Ovarian Cancer

To evaluate if CT features at diagnosis in patients with HGSOC can be used to build an Artificial Intelligence model capable of discerning the pathological involvement of the mesentery, assessing the potential impediments for an optimal debulking surgery and predicting the development of resistance to platinum based chemotherapeutic agents.

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Key information

Conditions

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

Age range

18 year and older

Sex eligibility

Female

Study type

Observational

Primary location

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Women with confirmed HGSOC wiht mesenteric involvment
  • Age > 18 years
  • FIGO STAGE IIIB-IV
  • Primary diagnosis
  • Signed informed consent

Exclusion criteria

  • Non-serous high grade epithelial ovarian cancer (serous low grade, mucinous, clear cell carcinoma, endometrioid or non-epithelial ovarian cancer)
  • Early stage disease (I and II stage)
  • CT scan not available
  • Non-primary diagnosis or patient subjected to neoadjuvant chemotherapy

Treatment and study plan

Computed Tomography

Diagnostic Test

Computed Tomography done according to Clinical Practice to assess mesenteric involvment

Primary outcomes

  1. Preoperative Artificial Intelligence assisted CT-based evaluation

    Time frame: 1 year

    Preoperative Artificial Intelligence assisted CT-based prediction of patients with suboptimal debulking at surgery due to diffuse mesenteric disease or mesenteric retraction.

Secondary outcomes

  1. Evaluation of the Radiologist Assessment of the CT

    Time frame: 1 year

    Identification of mesenteric infiltration from CT images using Artificial Intelligence at a comparable performance with human/radiologist assessment.

  2. Prediction of Platinum Resistance

    Time frame: 1 year

    AI-assisted CT-based prediction of patients who will develop platinum resistance

  3. Prediction of Progression Free Survival (PFS) and Overall Survival (OS)

    Time frame: 2 years

    Prediction of Progression Free Survival (PFS) and Overall Survival (OS) with Artificial Intelligence

Study contacts

Contact information is provided by the study sponsor or research team.

Camilla Panico, Dr

CONTACT

[email protected]

+390630155701

Sponsors and collaborators

Lead sponsor

Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Other

Collaborators

  • Danube University Krems
  • Ente Ospedaliero Cantonale, Ticino, Switzerland
  • Institut du Cancer de Montpellier - Val d'Aurelle

Registry information

Official study title

CT Detected Tumour Infiltration Patterns of the Mesentery in High Grade Ovarian Carcinoma (HGSOC) Patients, Their Role in Treatment Planning and Outcome Prediction and How Machine Learning Can be Applied to Identify Them.

Acronym: MIO

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Mar 26, 2024
Registry last updated
Mar 12, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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