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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT06324175
Other study ID # 6353
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date February 1, 2024
Est. completion date December 31, 2026

Study information

Verified date February 2024
Source Fondazione Policlinico Universitario Agostino Gemelli IRCCS
Contact Camilla Panico, Dr.
Phone +390630158637
Email camilla.panico@policlinicogemelli.it
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Recruitment information / eligibility

Status Recruiting
Enrollment 24
Est. completion date December 31, 2026
Est. primary completion date December 31, 2025
Accepts healthy volunteers No
Gender Female
Age group 18 Years and older
Eligibility 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


Locations

Country Name City State
Italy Advanced Radiology Center Roma

Sponsors (1)

Lead Sponsor Collaborator
Fondazione Policlinico Universitario Agostino Gemelli IRCCS

Country where clinical trial is conducted

Italy, 

Outcome

Type Measure Description Time frame Safety issue
Primary Implementation of the 3D printing pipeline in the clinical setting for recurrent HGSOC 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. 3 years
Secondary Biological validation of spatial radiomics in HGSOC 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. 3 years
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