Multidimensional Diffusion Magnetic Resonance Imaging for Characterization of Tissue Microstructure in Breast Cancer Patients: A Prospective Pilot Study.

Isaac Daimiel Naranjo,Karin Bryskhe,Roberto Lo Gullo,Jeffrey S Reiner,Daniel Topgaard,Dilip D Giri,Alexis Reymbaut,Katja Pinker-Domenig,Patrik Brynolfsson,Sunitha B Thakur

doi:10.3390/cancers13071606

Abstract

Simple SummaryIn this prospective pilot study, we investigated the potential of clinical multidimensional diffusion magnetic resonance imaging (MDD MRI) for the microstructural characterization of breast cancers and normal fibroglandular breast tissue. The method relies on advanced gradient waveforms to encode the signal with information about cell densities, shapes, and orientations, and to quantify tissue composition as a probability distribution of diffusion tensors in a space with dimensions analogous to those on a cellular level. Sixteen patients with breast cancer underwent MDD MRI within a clinically feasible scan time of approximately 4 min, providing voxel-resolved distributions and parameter maps with microstructural information that is not accessible with conventional methods.Diffusion-weighted imaging is a non-invasive functional imaging modality for breast tumor characterization through apparent diffusion coefficients. Yet, it has so far been unable to intuitively inform on tissue microstructure. In this IRB-approved prospective study, we applied novel multidimensional diffusion (MDD) encoding across 16 patients with suspected breast cancer to evaluate its potential for tissue characterization in the clinical setting. Data acquired via custom MDD sequences was processed using an algorithm estimating non-parametric diffusion tensor distributions. The statistical descriptors of these distributions allow us to quantify tissue composition in terms of metrics informing on cell densities, shapes, and orientations. Additionally, signal fractions from specific cell types, such as elongated cells (bin1), isotropic cells (bin2), and free water (bin3), were teased apart. Histogram analysis in cancers and healthy breast tissue showed that cancers exhibited lower mean values of “size” (1.43 ± 0.54 × 10−3 mm2/s) and higher mean values of “shape” (0.47 ± 0.15) corresponding to bin1, while FGT (fibroglandular breast tissue) presented higher mean values of “size” (2.33 ± 0.22 × 10−3 mm2/s) and lower mean values of “shape” (0.27 ± 0.11) corresponding to bin3 (p < 0.001). Invasive carcinomas showed significant differences in mean signal fractions from bin1 (0.64 ± 0.13 vs. 0.4 ± 0.25) and bin3 (0.18 ± 0.08 vs. 0.42 ± 0.21) compared to ductal carcinomas in situ (DCIS) and invasive carcinomas with associated DCIS (p = 0.03). MDD enabled qualitative and quantitative evaluation of the composition of breast cancers and healthy glands.

Highlights

The continuous evolution and versatility of diffusion-weighted imaging (DWI) have made this technique a valuable tool that is increasingly used in breast cancer imaging with a wide variety of applications [1]
The small number of observations within categories for tumor grade, human epidermal growth factor receptor 2 (HER2), and hormonal receptor status prevented more detailed correlation of Diffusion tensor distribution (DTD)-specific metrics with these features of invasive cancers. In this prospective pilot study, we investigated the potential of clinical multidimensional diffusion (MDD) MRI for the microstructural characterization of breast cancers and normal fibroglandular breast tissue
Our results showed that cancers were characterized by low E[Diso] and high E[D∆2], corresponding to bin1, while normal fibroglandular breast tissue (FGT) exhibited high E[Diso] and low E[D∆2], corresponding to bin3

Summary

Introduction

The continuous evolution and versatility of diffusion-weighted imaging (DWI) have made this technique a valuable tool that is increasingly used in breast cancer imaging with a wide variety of applications [1]. Current clinical conventional DWI methods, such as trace DWI, quantify water mobility in terms of a single apparent diffusion coefficient (ADC) per imaging voxel, which inversely reports on voxel-averaged cell density [2,3]. Single ADCs per voxel cannot capture the non-Gaussian diffusion characterizing “heterogeneous tissues”, i.e., tissues comprising distinct cell types and orientations [5,6,7] Another conventional approach to interpreting DWI is diffusion tensor imaging (DTI) [8]. This method describes tissues in terms of a single diffusion tensor averaged over the voxel scale. Other approaches have revealed nonparametric cell size distribution, this limitation has not been addressed yet [18,19]

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