Clinically feasible liver tumour cell size measurement through histology-informed in vivo diffusion MRI

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. 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. 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). Our biologically meaningful approach may complement standard-of-care radiology, and become a new tool for enhanced cancer characterisation in precision oncology. Magnetic Resonance Imaging (MRI) is an imaging tool used frequently to detect and monitor malignant tumours. Notably, the latest MRI technology enables physicians to measure not only the size of a tumour, but even properties related to the cells it contains. This information, like number or size of cells can help oncologists to choose the best treatment. Obtaining this information using this imaging tool in tumors that have spread to the liver, remained challenging. Here we present an MRI method developed specifically to capture the biology of liver tumour cells. We base it on mathematical models that use information gathered from microscopy images – known as histology, where individual cells are visible – to guide the analysis of the scan. We show that the method quantifies tumour cell size and density from a simple, clinically feasible MRI scan and propose this technique for oncological applications. Grussu et al. use co-localised MRI and histology data to design a practical MRI technique for cell size and density measurement in liver tumours in vivo. The method provides non-invasive proxies of histological properties that are associated to cell proliferation, that explain tumour volume and that distinguish liver tumour types.