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Data-driven identification of tumor subregions based on intravoxel incoherent motion reveals association with proliferative activity

Journal article
Authors Oscar Jalnefjord
Mikael Montelius
Jonathan Arvidsson
Eva Forssell-Aronsson
Göran Starck
Maria Ljungberg
Published in Magnetic Resonance in Medicine
Volume 82
Issue 4
Pages 1480-1490
ISSN 0740-3194
Publication year 2019
Published at Institute of Clinical Sciences, Department of Radiation Physics
Pages 1480-1490
Language en
Keywords clustering, diffusion, histology, IVIM, MRI, perfusion, contrast-enhanced mri, human-melanoma xenografts, dce-mri, cluster-analysis, intratumor heterogeneity, model, diffusion, cancer, perfusion, quantification, Radiology, Nuclear Medicine & Medical Imaging
Subject categories Radiology


Purpose: Intravoxel incoherent motion (IVIM) analysis gives information on tissue diffusion and perfusion and may thus have a potential for e.g. tumor tissue characterization. This work aims to study if clustering based on IVIM parameter maps can identify tumor subregions, and to assess the relevance of obtained subregions by histological analysis. Methods: Fourteen mice with human neuroendocrine tumors were examined with diffusion-weighted imaging to obtain IVIM parameter maps. Gaussian mixture models with IVIM maps from all tumors as input were used to partition voxels into k clusters, where k = 2 was chosen for further analysis based on goodness of fit. Clustering was performed with and without the perfusion-related IVIM parameter D*, and with and without including spatial information. The validity of the clustering was assessed by comparison with corresponding histologically stained tumor sections. A Ki-67-based index quantifying the degree of tumor proliferation was considered appropriate for the comparison based on the obtained cluster characteristics. Results: The clustering resulted in one class with low diffusion and high perfusion and another with slightly higher diffusion and low perfusion. Strong agreement was found between tumor subregions identified by clustering and subregions identified by histological analysis, both regarding size and spatial agreement. Neither D* nor spatial information had substantial effects on the clustering results. Conclusions: The results of this study show that IVIM parameter maps can be used to identify tumor subregions using a data-driven framework based on Gaussian mixture models. In the studied tumor model, the obtained subregions showed agreement with proliferative activity.

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