Higher‐order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer

Abstract

Purpose Mesorectal lymph node staging plays an important role in treatment decision making. Here, we explore the benefit of higher‐order diffusion MRI models accounting for non‐Gaussian diffusion effects to classify mesorectal lymph nodes both 1) ex vivo at ultrahigh field correlated with histology and 2) in vivo in a clinical scanner upon patient staging. Methods The preclinical investigation included 54 mesorectal lymph nodes, which were scanned at 16.4 T with an extensive diffusion MRI acquisition. Eight diffusion models were compared in terms of goodness of fit, lymph node classification ability, and histology correlation. In the clinical part of this study, 10 rectal cancer patients were scanned with diffusion MRI at 1.5 T, and 72 lymph nodes were analyzed with Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), Kurtosis, and IVIM‐Kurtosis. Results Compartment models including restricted and anisotropic diffusion improved the preclinical data fit, as well as the lymph node classification, compared to standard ADC. The comparison with histology revealed only moderate correlations, and the highest values were observed between diffusion anisotropy metrics and cell area fraction. In the clinical study, the diffusivity from IVIM‐Kurtosis was the only metric showing significant differences between benign (0.80 ± 0.30 μm2/ms) and malignant (1.02 ± 0.41 μm2/ms, P = .03) nodes. IVIM‐Kurtosis also yielded the largest area under the receiver operating characteristic curve (0.73) and significantly improved the node differentiation when added to the standard visual analysis by experts based on T2‐weighted imaging. Conclusion Higher‐order diffusion MRI models perform better than standard ADC and may be of added value for mesorectal lymph node classification in rectal cancer patients.

Cellular EXchange Imaging (CEXI): Evaluation of a diffusion model including water exchange in cells using numerical phantoms of permeable spheres

“…Healthy lymph nodes (Figure 1, left magnification) are homogeneous tissues composed of round lymphocytes that are known to be permeable (Cheung et al, 1982;Grinstein et al, 1983;Wesselborg and Kabelitz, 1993). In the hematoxylin and eosin stain of Figure 1, we measured using QuPath (ban, 2017) a cell area of 56 ± 24µm 2 , corresponding to a radius from 3 to 5µm in perfectly spherical cells, similar to previously reported values (Ianus ¸et al, 2020). In tumors (Figure 1, right magnification), the tissue is more heterogeneous with the presence of blood vessels and conjunctive tissue.…”

“…These spheres had their radii normally distributed around a mean radius R s equal to {2; 3; 4; 5; 8}µm with a standard deviation of 1% of the radius. For all substrates, the intracellular volume fraction ICVF reached 0.65, corresponding to the value reported in malignant lymph nodes (Ianus ¸et al, 2020).…”

“…The present work focuses on membrane permeability in spherical cells to capture the effect of exchange on microstructure estimation. Our substrates are isotropic, but a previous study (Ianus ¸et al, 2020) showed that the biophysical model that accounts for anisotropic diffusion had better results in lymph node discrimination. However, we decided to focus on the effect of exchange, and future work will combine exchange and anisotropic diffusion to better capture the complexity of lymph nodes' microstructure.…”

“…The application that motivates this study is the discrimination of healthy from cancerous lymph nodes using non-invasive imaging. Several studies investigated the potential of dMRI to replace invasive procedures such as biopsy for lymph node metastases diagnosis, particularly the apparent diffusion coefficient (ADC) in breast (Zaiton et al, 2016;Zhao et al, 2020), neck (Suh et al, 2018), and colorectal (Yasui et al, 2009) cancer, or more advanced biophysical models for benign and malignant tumor discrimination in lymph nodes (Ianus ¸et al, 2020).…”

“…One of the challenges of biophysical models is their specificity to one particular tissue, which requires rethinking the optimal model and assumptions that best capture its features. For example, blood displacement inside the tumor vessels (Panagiotaki et al, 2014;Ianus ¸et al, 2020;Sahalan, 2018;Iima et al, 2014) induces anisotropic diffusion, which usually is not accounted for in the healthy white matter.…”