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

Abstract

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.

Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends

“…Conversely, IVIM parameter maps obtained by clustering approaches with Gaussian mixture models might be useful for the identification of tumor subregions with proliferative activity (Ki-67 index). 39 Still, the correlation between IVIM parameters ( f or f · D * ) and microvascular histology is not entirely clear, and further studies are needed to validate the correlation, both in humans and animals.…”

VERDICT MRI for radiation treatment response assessment in neuroendocrine tumors

“…Such methods come with the risk of yielding results that are difficult to interpret biologically. Still, clustering analysis has shown promising results when used with the IVIM model and may be a method of interest for future studies using VERDICT 14 …”

“…Still, clustering analysis has shown promising results when used with the IVIM model and may be a method of interest for future studies using VERDICT. 14…”

“…To find parameters with higher specificity, analysis of dMRI with more advanced mathematical representations of the MR signal has received increased attention during recent decades, with new parameters derived from dMRI being investigated beyond the . Two promising techniques increasingly used in cancer research are intravoxel incoherent motion (IVIM) imaging 14–16 and diffusion kurtosis imaging (DKI) 17,18 . IVIM uses a combination of data acquired at low (≤200 s/mm 2 ) and medium‐high (200–1000 s/mm 2 ) b‐values to separate diffusion from the pseudorandom microcirculation of blood, which allows for estimation of perfusion‐related parameters 19 .…”

Desenvolvimento e implementação de métodos de processamento de imagens por RM para obtenção de parâmetros relacionados à perfusão e à difusão

“…Uma possibilidade de solução é a aplicação de filtros nas imagens adquiridas antes que se faça a estimativa de parâmetros, com o intuito de aumentar o SNR. 71,[94][95][96] Também há estudos que constatam a melhoria do valor de SNR dos sinais de IVIM caso sejam usados métodos de aquisição de múltiplas fatias simultaneamente com sequências rápidas de aquisição, como a EPI (SMS-DWI). 97 Mesmo assim, os requisitos de ruído para estudo com IVIM são altos e também variam conforme o tecido analisado, pois a vascularização do tecido rege a intensidade do sinal adquirido, o que interfere diretamente no SNR.…”