Histology-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 data-driven estimation of properties such as the intrinsic extra-cellular diffusivity, cell size or cell membrane permeability. We compare Histo-μSim to metrics from well-established analytical multi-compartment models in silico, on fixed mouse tissues scanned ex vivo (kidneys, spleens, and breast tumours) and in cancer patients in vivo. Results suggest that Histo-μSim is feasible in clinical settings, and that it delivers metrics that more accurately reflect histology as compared to analytical models. In conclusion, Histo-μSim offers histologically-meaningful tissue descriptors that may increase the specificity of dMRI towards cancer, and thus play a crucial role in precision oncology.
Authors
- María Vieito (ORCID: https://orcid.org/0000-0001-7531-343X)
- Kinga Bernatowicz (ORCID: https://orcid.org/0000-0001-9166-1709)
- Xavier Merino (ORCID: https://orcid.org/0000-0002-1985-9363)
- Núria Rosón (ORCID: https://orcid.org/0000-0001-6751-7491)
- Raquel Pérez-López (ORCID: https://orcid.org/0000-0002-9176-0130)
- Ignasi Barba (ORCID: https://orcid.org/0000-0001-9886-3776)
- Rodrigo Almeida Toledo (ORCID: https://orcid.org/0000-0003-3070-6795)
- Francesco Grussu (ORCID: https://orcid.org/0000-0002-0945-3909)
- Manuel Escobar (ORCID: https://orcid.org/0000-0002-3794-2224)
- Alba Escriche
- Roser Sala‐Llonch (ORCID: https://orcid.org/0000-0003-3576-0475)
- Dmitry S. Novikov (ORCID: https://orcid.org/0000-0002-4213-3050)
- Garazi Serna (ORCID: https://orcid.org/0000-0003-2666-0982)
- Sara Simonetti (ORCID: https://orcid.org/0000-0003-2406-030X)
- Elena Garralda (ORCID: https://orcid.org/0000-0002-4412-7842)
- María Abad (ORCID: https://orcid.org/0000-0002-1462-2498)
- Marco Palombo (ORCID: https://orcid.org/0000-0003-4892-7967)
- Els Fieremans (ORCID: https://orcid.org/0000-0002-1384-8591)
- Richard Mast (ORCID: https://orcid.org/0000-0001-6005-800X)
- Paolo Giovanni Nuciforo (ORCID: https://orcid.org/0000-0003-1380-0990)
- Carlos Macarro (ORCID: https://orcid.org/0009-0000-0597-9316)
- Athanasios Grigoriou (ORCID: https://orcid.org/0000-0003-1826-8040)
- Emanuela Greco
- Anna Voronova (ORCID: https://orcid.org/0009-0006-9600-0436)
- Daniel Navarro-Garcia
Institutions
- Universitat de Vic - Universitat Central de Catalunya (ES)
- Hebron University (PS)
- Institut d'Estadística de Catalunya (ES)
- Vall d'Hebron Institut de Recerca (ES)
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (ES)
- Institute for Research in Biomedicine (ES)
- Vall d'Hebron Hospital Universitari (ES)
- Consorci Institut D'Investigacions Biomediques August Pi I Sunyer (ES)
- Vall d'Hebron Institute of Oncology (ES)
- New York University (US)
- Universitat de Barcelona (ES)
- Cardiff University (GB)
Publication Details
- Journal
- Communications Biology
- Published
- 2025-11-26
- DOI
- https://doi.org/10.1038/s42003-025-09096-3
- Primary Topic
- MRI in cancer diagnosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00