Carlos Macarro

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.

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.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To identify precise 3D radiomics features in CT images that enable computation of stable and biologically meaningful habitats with machine learning for cancer heterogeneity assessment. Materials and Methods This retrospective study included 2436 liver or lung lesions from 605 CT scans (November 2010-December 2021) in 318 patients with cancer (mean age, 64.5 years ± 10.1 [SD]; 185 male patients). 3D radiomics were computed from original and perturbed (simulated retest) images with different combinations of feature computation kernel radius (R) and bin size (B). The lower 95% confidence limit (LCL) of the intraclass correlation coefficient was used to measure repeatability and reproducibility. Precise features were identified by combining repeatability and reproducibility results (LCL ≥ 0.50). Habitats were obtained with Gaussian Mixture Models in original and perturbed data using precise radiomics features and compared with habitats obtained using all features. The Dice similarity coefficient (DSC) was used to assess habitat stability. Biologic correlates of CT habitats were explored in a case study, with a cohort of 13 patients with CT, multiparametric MRI (mpMRI) and tumor biopsies. Results 3D radiomics showed poor repeatability (median LCL[IQR] of 0.442 [0.312-0.516]) and poor reproducibility against R (0.44[0.33-0.526]) but excellent reproducibility against B (0.929[0.853-0.988]). Twentysix radiomics features were precise, differing in lung and liver lesions. Habitats obtained with precise features (DSC, 0.601 (0.494-0.712) and 0.651 (0.52-0.784) for lung and liver lesions, respectively) were more stable than those obtained with all features (DSC, 0.532 (0.424-0.637) and 0.587 (0.465-0.703), respectively; P < .001). In the case study, CT habitats correlated quantitatively and qualitatively, with heterogeneity observed in mpMRI habitats and histology. Conclusion Precise 3D radiomics features were identified on CT that enabled tumor heterogeneity assessment through stable tumor habitat computation. ©RSNA, 2024.