Year: 2024
Type: conference-paper
Abstract: Motivation: Intracellular volume fraction (fIC) maps from VERDICT-MRI have shown potential to improve prostate cancer (PCa) stratification, but the microstructural origin of the signal has not yet been investigated in in vivo settings. Goal(s): Investigate the accuracy of fIC from in vivo VERDICT-MRI as a measurement of cell density using matched prostatectomy specimens. Approach: Using personalised moulds from multiparametric (mp)MRI and deep learning image registration, we align whole prostatectomy histology images with corresponding VERDICT MR images. We compare fIC maps against cell density maps derived from histology. Results: fIC maps show very strong agreement with histology-derived cell density maps of epithelial cells (r=0.8303). Impact: Our study shows that VERDICT fIC maps are accurate descriptors of epithelial cell density in the prostate. The biological interpretability of these maps will facilitate translation into clinical practice, improving PCa stratification from MRI.
DOI: 10.58530/2024/0588
Authors: Marta Masramon Munoz, Manju Mathew, Saurabh Singh, Thomy Mertzanidou, Shipra Suman, Joey Clemente, Adam Retter, Marianthi-Vasilik Papoutsaki, Lorna Smith, Francesco Grussu, Veeru Kasivisvanathan, Alistair Grey, Eoin Dineen, Greg Shaw, Martyn Carter, Dominic Patel, Lucy Caselton, Caroline M. Moore, David Atkinson, Aiman Haider, Alex Freeman, Daniel Alexander, Shonit Punwani, Eleftheria Panagiotaki
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Learning MRI and histology image mappings for cancer diagnosis and prognosis, G3958697817, Using imaging, blood and urinary biomarkers to improve the diagnosis of aggressive prostate cancer, EPSRC Centre for Doctoral Training in Intelligent, Integrated Imaging In Healthcare (i4health), G6127694706, Non-invasive MRI Biomarkers for Oncology
OpenAlex ID: W4404749197