Ianus, A; Santiago, I; Galzerano, A; Montesinos, P; Loucao, N; Sanchez-Gonzalez, J; Alexander, DC; ... Shemesh, N; + view all Ianus, A; Santiago, I; Galzerano, A; Montesinos, P; Loucao, N; Sanchez-Gonzalez, J; Alexander, DC; Matos, C; Shemesh, N; - view fewer (2020) Higher-order diffusion MRI characterization of mesorectal lymph nodes in rectal cancer. Magnetic Resonance in Medicine , 84 (1) pp. 348-364. 10.1002/mrm.28102.

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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 μm^{2} /ms) and malignant (1.02 ± 0.41 μm^{2} /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 T_{2}‐weighted imaging.

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