Spatial Radiogenomics of Ovarian Cancer: Implementation of a Lesion-specific 3D-printed Mould Pipeline in the Clinical Workflow for Image-guided Tissue Multi-sampling of Ovarian Tumours - Trial NCT06324175

Timeline & Enrollment

Primary Outcome

Implementation of the 3D printing pipeline in the clinical setting for recurrent HGSOC

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.

ICD-10 Classifications