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Histology-informed microstructural diffusion simulations for MRI cancer characterisation—the Histo-μSim framework.

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Title: Histology-informed microstructural diffusion simulations for MRI cancer characterisation—the Histo-μSim framework.
Authors: Grigoriou, Athanasios, Macarro, Carlos, Palombo, Marco, Navarro-Garcia, Daniel, Voronova, Anna Kira, Bernatowicz, Kinga, Barba, Ignasi, Escriche, Alba, Greco, Emanuela, Abad, María, Simonetti, Sara, Serna, Garazi, Mast, Richard, Merino, Xavier, Roson, Núria, Escobar, Manuel, Vieito, Maria, Nuciforo, Paolo, Toledo, Rodrigo, Garralda, Elena
Source: Communications Biology; 11/26/2025, Vol. 8 Issue 1, p1-27, 27p
Abstract: 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. A histology-informed, diffusion Magnetic Resonance Imaging simulation framework improves the non-invasive assessment of cancer biology in solid tumours in vivo [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract: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. A histology-informed, diffusion Magnetic Resonance Imaging simulation framework improves the non-invasive assessment of cancer biology in solid tumours in vivo [ABSTRACT FROM AUTHOR]
ISSN:23993642
DOI:10.1038/s42003-025-09096-3