Mesenteric Infiltration in Ovarian Cancer

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.

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06331130

Acronym

MIO

Enrollment

510

Registered

2024-03-26

Start date

2024-03-01

Completion date

2025-12-31

Last updated

2025-03-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Ovary Cancer

Brief summary

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.

Interventions

DIAGNOSTIC_TESTComputed Tomography

Computed Tomography done according to Clinical Practice to assess mesenteric involvment

Study design

Observational model

OTHER

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

FEMALE

Age

18 Years to No maximum

Inclusion criteria

1. Women with confirmed HGSOC wiht mesenteric involvment 2. Age \> 18 years 3. FIGO STAGE IIIB-IV 4. Primary diagnosis 5. Signed informed consent

Exclusion criteria

1. Non-serous high grade epithelial ovarian cancer (serous low grade, mucinous, clear cell carcinoma, endometrioid or non-epithelial ovarian cancer) 2. Early stage disease (I and II stage) 3. CT scan not available 4. Non-primary diagnosis or patient subjected to neoadjuvant chemotherapy

Design outcomes

Primary

Measure	Time frame	Description
Preoperative Artificial Intelligence assisted CT-based evaluation	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

Measure	Time frame	Description
Evaluation of the Radiologist Assessment of the CT	1 year	Identification of mesenteric infiltration from CT images using Artificial Intelligence at a comparable performance with human/radiologist assessment.
Prediction of Platinum Resistance	1 year	AI-assisted CT-based prediction of patients who will develop platinum resistance
Prediction of Progression Free Survival (PFS) and Overall Survival (OS)	2 years	Prediction of Progression Free Survival (PFS) and Overall Survival (OS) with Artificial Intelligence

Countries

Italy

Contacts

Primary ContactCamilla Panico, Dr

camilla.panico@policlinicogemelli.it+390630155701

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026