biostudies-literatureGrigoriou ABanco Bilbao Vizcaya Argentaria (BBVA)"la Caixa" Foundation (Caixa Foundation)Ministry of Economy and Competitiveness | Instituto de Salud Carlos III (Institute of Health Carlos III)Prostate Cancer Foundation (PCF)AstraZenecaDepartament d'Innovació, Universitats i Empresa, Generalitat de Catalunya (Department of Innovation, Education and Enterprise, Government of Catalonia)Ministry of Economy and Competitiveness | Agencia Estatal de Investigación (Spanish Agencia Estatal de Investigación)Fundación Científica Asociación Española Contra el Cáncer (Scientific Foundation, Spanish Association Against Cancer)EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)EC | Directorate-General for Employment, Social Affairs and Inclusion | European Social Fund (Fondo Social Europeo)1695https://www.ebi.ac.uk/biostudies/studies/S-EPMC12657972biostudies-literatureUnknown8(1)Diffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the microscopic complexity of human tissues enable the development of innovative biomarkers with unprecedented fidelity to histology. Simulation-informed dMRI has traditionally focussed on brain imaging, and it has neglected other applications, as for example body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging: the Histo-μSim approach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the daCommunications biologyHistology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework.PMC12657972CEX2020-001024-S / AEI / 10.13039 / 501100011033PREdICTPI18/0139518YOUN19LCF/BQ/PR22/11920010PRYCO211023SERRPRE2022-102586CaixaResearch Advanced OncologyPI21/010198013702019 BP 001822023PROD00178A way to make Europe89/2017Grigoriou AEscriche AVoronova AKSala-Llonch RBernatowicz KSimonetti SNuciforo PBarba IToledo RPerez-Lopez REscobar MGrussu FFieremans ENovikov DSSerna GNavarro-Garcia DGreco EAbad MMacarro CRoson NMast RVieito MGarralda EMerino XPalombo MfalseHistology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework.Diffusion Magnetic Resonance Imaging (dMRI) simulations in geometries mimicking the microscopic complexity of human tissues enable the development of innovative biomarkers with unprecedented fidelity to histology. Simulation-informed dMRI has traditionally focussed on brain imaging, and it has neglected other applications, as for example body cancer imaging, where new non-invasive biomarkers are still sought. This article fills this gap by introducing a Monte Carlo diffusion simulation framework informed by histology, for enhanced body dMR microstructural imaging: the Histo-μSim approach. We generate dictionaries of synthetic dMRI signals with coupled tissue properties from virtual cancer environments, reconstructed from hematoxylin-eosin stains of human liver biopsies. These enable the da2025-01-01T00:00:00Z2025 Nov2026-06-05T21:12:27.253Z2026-05-21T03:13:19.728ZS-EPMC126579724129880910.1038/s42003-025-09096-3