Grussu, Francesco, Grigoriou, Athanasios, Bernatowicz, Kinga, Palombo, Marco  ORCID: https://orcid.org/0000-0003-4892-7967, Casanova-Salas, Irene, Navarro-Garcia, Daniel, Barba, Ignasi, Simonetti, Sara, Serna, Garazi, Macarro, Carlos, Voronova, Anna Kira, Garay, Valezka, Corral, Juan Francisco, Vidorreta, Marta, García-Polo García, Pablo, Merino, Xavier, Mast, Richard, Rosón, Núria, Escobar, Manuel, Vieito, Maria, Toledo, Rodrigo, Nuciforo, Paolo, Mateo, Joaquin, Garralda, Elena and Perez-Lopez, Raquel
2025.
Clinically feasible liver tumour cell size measurement through histology-informed in vivo diffusion MRI.
Communications Medicine
10.1038/s43856-025-01246-2
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Abstract
Background: Innovative diffusion Magnetic Resonance Imaging (MRI) models enable the non-invasive measurement of cancer biological properties in vivo. However, while cancers frequently spread to the liver, models tailored for liver application and easy to deploy in the clinic are still sought. We fill this gap by delivering a practical, clinically-viable framework for liver tumour diffusion imaging, informing its design through histology. Methods: We compare MRI and histological data from mice and cancer patients, namely: MRI and hemaotxylin-eosin (HE) stains from N = 7 fixed mouse livers; MRI of N = 38 patients suffering from liver solid tumours, N = 18 of whom with HE biopsies. We study five diffusion models, ranking them according to a total MRI-histology correlation score. Afterwards, we test metrics from the top-ranking model on our cohort, assessing their sensitivity to cell proliferation (Ki-67 staining, N = 10), evaluating their association with tumour volume (N = 140 tumours), and comparing them across primary cancer types. Results: We select a dMRI signal model of restricted intra-cellular diffusion with negligible extra-cellular contributions, which maximises radiological-histological correlations (total score: 0.625). The model provides cell size and density estimates that i) correlate with histology (e.g., for cell size: r = 0.44, p = 0.029), ii) are associated to Ki-67 cell proliferation (for MRI cell density: r = 0.80, p = 0.006) and tumour volume (r = 0.40, p < 10–5 for tumour volume regression), and iii) that distinguish melanoma (N = 8) from colorectal cancer (N = 13) (p = 0.011 for intra-cellular fraction). Conclusions: Our biologically meaningful approach may complement standard-of-care radiology, and become a new tool for enhanced cancer characterisation in precision oncology.
| Item Type: | Article |
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| Date Type: | Published Online |
| Status: | In Press |
| Schools: | Schools > Computer Science & Informatics Schools > Psychology Research Institutes & Centres > Cardiff University Brain Research Imaging Centre (CUBRIC) |
| Publisher: | Nature Research |
| Date of First Compliant Deposit: | 11 November 2025 |
| Date of Acceptance: | 5 November 2025 |
| Last Modified: | 02 Dec 2025 14:47 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/182342 |
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