Advanced Diffusion‐Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology

Abstract

Diffusion‐weighted magnetic resonance imaging (DW‐MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW‐MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state‐of‐the‐art of DW‐MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW‐MRI method is essential. In this article, we review the current landscape of DW‐MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b‐tensor diffusion encoding or joint diffusion‐relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter‐/intra‐scanner robustness, and on assessing histological validity in broader contexts.Level of EvidenceNATechnical EfficacyStage 2

Histology-informed liver diffusion MRI: relevance in cancer immunotherapy

“…We benchmarked the proposed dMRI approach against well-established DKI apparent diffusion and kurtosis coefficients ( ADC and K ) 30 . ADC and K are easy to compute with compact dMRI acquisitions, and are sensitive to cancer cellularity 46 , a fact confirmed by our data. However, in our cohort, neither of ADC and K showed statistically significant associations with clinical PFS.…”

“…In this study, we benchmarked the proposed dMRI approach against well-established DKI apparent diffusion and kurtosis coefficients ( ADC and K ) ( 30 ). ADC and K have shown utility in clinical settings, being easy to compute with compact dMRI acquisitions, and sensitive, for example, to cancer cellularity (negative/positive correlation for ADC / K ( 46 ), a result confirmed by the correlations detected in this study, namely: r = –0.47 between ADC and CD histo , r = 0.43 between K and CD histo ). However, in our cohort, neither of ADC and K showed statistically significant associations with the clinical outcome (PFS).…”

“…However, in our cohort, neither of 𝐴𝐴𝐴𝐴𝐶𝐶 and 𝐾𝐾 showed statistically significant associations with the clinical outcome (PFS). This may due, at least partly, to the fact that 𝐴𝐴𝐴𝐴𝐶𝐶 and 𝐾𝐾 are semi-quantitative, protocoldependent metrics, whose value can change as function of, for example, the diffusion time (46,54). Here, we did not perform any inter-scanner harmonisation delibterately, to stress the quantitative nature of our cell size/density mapping approach, which inherently accounts for inter-scanner protocol differences.…”

“…Notably, our finding partially disagrees with some of the latest trends in body dMRI development. Recent literature has been characterised by the introduction of ever-complex biophysical models, featuring increasingly high numbers of tissue parameters to estimate ( 21, 36, 37, 46 ). While more complex models better capture the true degrees of freedom on in vivo dMRI signals, they have the disadvantage of requiring longer and/or more sophisticated acquisition protocols to enable their fitting ( 26 ), a fact that negatively impacts on their clinical applicability.…”

“…The latest liver dMRI models aim to disentangle intra-cellular and extra-cellular water contributions to the total signal 6,19,22,23 . This powerful approach enables the estimation of innovative tissue property maps, but its clinical deployment is hampered by the high number of unknown tissue parameters to estimate, which requires impractically long dMRI acquisitions 19,45,46 . With this challenge in mind, this paper delivers a practical implementation of a two-compartment dMRI signal model, tailored for liver imaging, and truly feasible in the clinic.…”