Validating the biological origin of in vivo fractional intracellular volume from VERDICT-MRI in the prostate
Year: 2025
Type: preprint
Study design: Observational Study
Abstract: Reliable imaging biomarkers are needed to characterize clinically significant prostate cancer (csPCa). The clinically established measure, apparent diffusion coefficient (ADC) from multiparametric (mp)MRI, lacks specificity to key histological features. The fractional intracellular volume (fIC) from in vivo Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumors (VERDICT)-MRI has shown improved detection of csPCa. This prospective study (NCT04792138) quantifies the accuracy of fIC as a measure of prostate epithelial cell density. Fifty-two participants (mean age, 65 years ± 6; Gleason grades ≥ 3 + 4) with biopsy-confirmed PCa underwent mpMRI and VERDICT-MRI before radical prostatectomy. Personalized molds from preoperative mpMRI enabled histology to MRI correspondence. Comparisons between histological epithelial density measures and MR markers were made on a region of interest (ROI) basis using Pearson’s correlation coefficient. VERDICT fIC corresponded more strongly with epithelial fraction, cell density and epithelial density (r = 0.784, 0.711, 0.747) than both VERDICT derived ADC (r=-0.639, -0.587, -0.609) and mpMRI ADC (r-0.353, -0.273, -0.326). Differences between benign tissue and csPCa in VERDICT fIC (0.16 ± 0.10 vs 0.51 ± 0.13) were similar to epithelial fraction (0.21 ± 0.08 vs 0.43 ± 0.12). These findings support in vivo VERDICT fIC as a marker of epithelial cell density and show promise for wider clinical use.
Source: Research Square
Authors: Marta Masramon, Manju Mathew, Saurabh Singh, Thomy Mertzanidou, Joey Clemente, Adam Retter, Natasha Thorley, Adam Phipps, Thomas Parry, Marianthi-Vasiliki Papoutsaki, Lorna Smith, Francesco Grussu, Veeru Kasivisvanathan, Alistair Grey, Eoin Dinneen, Greg Shaw, Dominic Patel, Lucy Caselton, Caroline M. Moore, David Atkinson, Aiman Haider, Alex Freeman, Daniel C. 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: W4415478663