Spatial Radiogenomics of Ovarian Cancer

Recruiting

• Conditions: Ovarian Cancer

• Registration Number: NCT06324175
• Lead Sponsor: Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Brief Summary

The biological spatial and temporal heterogeneity of High Grade Serous Ovarian Carcinoma (HGSOC) severely impacts the effectiveness of therapies and is a determinant of poor outcomes.

Current histological evaluation is made on a single tumour sample from a single disease site per patient thus ignoring molecular heterogeneity at the whole-tumour level, key for understanding and overcoming chemotherapy resistance. Imaging can play a crucial role in the development of personalised treatments by fully capturing the disease's heterogeneity.

Radiomics quantify the image information by capturing complex patterns related to the tissue microstructure. This information can be complemented with clinical data, liquid biopsies, histological markers and genomics ("radiogenomics") potentially leading to a better prediction of treatment response and outcome. However, the extracted quantitative features usually represent the entire tumour, ignoring the spatial context.

On the other hand, radiomics-derived imaging habitats characterize morphologically distinct tumour areas and are more appropriate for monitoring the changes in the tumour microenvironment over the course of therapy. In order to successfully incorporate the habitat-imaging approach to the clinic, histological and biological validation are crucial. However, histological validation of imaging is not a trivial task, due to issues such as unmatched spatial resolution, tissue deformations, lack of landmarks and imprecise cutting. Patient-specific three-dimensional (3D) moulds are an innovative tool for accurate co-registration between imaging and histology. The aim of this study is to optimize and integrate such an automated computational 3D-mould co-registration approach in the clinical work-flow in patients with HGSOC. The validated radiomics-based tumour habitats will also be used to guide tissue sampling to decipher their underlying biology using genomics analysis and explore novel prediction markers.

Detailed Description

Not available

Study Timeline

• Study Start: 2024-02-01
• Study First Submitted: 2024-02-05
• Study First Submitted QC: 2024-03-19
• Study First Posted: 2024-03-21
• Status Verified: 2025-03
• Last Update Submitted: 2025-03-11
• Last Update Posted: 2025-03-13
• Primary Completion: 2025-12-31
• Study Completion: 2026-12-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 24

Inclusion Criteria

• Patients with suspected HGSOC scheduled to undergo primary debulking surgery (PDS) or interval debulking surgery (IDS) will be recruited in the study. Prior histopathological confirmation of HGSOC will be required for IDS. The PDS cases without prior histological diagnosis will be selected on the basis of clinical suspicion (elevated serum CA125 and CT imaging).

Exclusion Criteria

• Patients less than 18 Years old
• Pregnancy
• 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 or MRI scan not available

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Implementation of the 3D printing pipeline in the clinical setting for recurrent HGSOC	3 years	Tumour will be segmented on the preoperative CT/MRI scan and 3D printed mould will be created from 2D images using a 3D printed machine. The 3D printed mould will be used to better oriented and analized the tumour in the surgery theatre in order to correlate anatomophathological features with Radiomics features that will be analyzed from the CT/MRI scans afterwords.

Secondary Outcome Measures

Name	Time	Method
Biological validation of spatial radiomics in HGSOC	3 years	Radiomic spatial texture analysis, such as the one shown in Figure 1B, will be used. The produced radiomics maps will then guide us in identifying the best biopsy sites, by recognizing phenotypically-distinct locations within complex tumours that are most likely to contain crucial information about diagnosis and treatment prognosis. The imaging information will then be linked to the genomic information of each distinct tumour habitat thus shedding more light on the underlying genomic heterogeneity of ovarian cancer and how it is phenotypically presented.

Trial Locations

Locations (1)

Advanced Radiology Center; 🇮🇹 Roma, Italy