Dataset Information

Microvascular MRI and unsupervised clustering yields histology-resembling images in two rat models of glioma.

ABSTRACT: Imaging heterogeneous cancer lesions is a real challenge. For diagnosis, histology often remains the reference, but it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in establishing a link between clinical in vivo imaging and the biologic properties of tissues. In this study, we propose to construct histology-resembling images based on tissue microvascularization, a magnetic resonance imaging (MRI) accessible source of contrast. To integrate the large amount of information collected with microvascular MRI, we combined a manual delineation of a spatial region of interest with an unsupervised, model-based cluster analysis (Mclust). This approach was applied to two rat models of glioma (C6 and F98). Six MRI parameters were mapped: apparent diffusion coefficient, vessel wall permeability, cerebral blood volume fraction, cerebral blood flow, tissular oxygen saturation, and cerebral metabolic rate of oxygen. Five clusters, defined by their MRI features, were found to correspond to specific histologic features, and revealed intratumoral spatial structures. These results suggest that the presence of a cluster within a tumor can be used to assess the presence of a tissue type. In addition, the cluster composition, i.e., a signature of the intratumoral structure, could be used to characterize tumor models as histology does.

SUBMITTER: Coquery N

PROVIDER: S-EPMC4126096 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Microvascular MRI and unsupervised clustering yields histology-resembling images in two rat models of glioma.

Coquery Nicolas N Francois Olivier O Lemasson Benjamin B Debacker Clément C Farion Régine R Rémy Chantal C Barbier Emmanuel Luc EL

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 20140521 8

Imaging heterogeneous cancer lesions is a real challenge. For diagnosis, histology often remains the reference, but it is widely acknowledged that biopsies are not reliable. There is thus a strong interest in establishing a link between clinical in vivo imaging and the biologic properties of tissues. In this study, we propose to construct histology-resembling images based on tissue microvascularization, a magnetic resonance imaging (MRI) accessible source of contrast. To integrate the large amou ...[more]

PMID: 24849664

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Project description:PurposeThis prospective study evaluated the use of vascular, extracellular and restricted diffusion for cytometry in tumours (VERDICT) MRI to investigate the tissue microstructure in glioma. VERDICT-derived parameters were correlated with both histological features and tumour subtype and were also used to explore the peritumoural region.MethodsFourteen consecutive treatment-naïve patients (43.5 years ± 15.1 years, six males, eight females) with suspected glioma underwent diffusion-weighted imaging including VERDICT modelling. Tumour cell radius and intracellular and combined extracellular/vascular volumes were estimated using a framework based on linearisation and convex optimisation. An experienced neuroradiologist outlined the peritumoural oedema, enhancing tumour and necrosis on T2-weighted imaging and contrast-enhanced T1-weighted imaging. The same regions of interest were applied to the co-registered VERDICT maps to calculate the microstructure parameters. Pathology sections were analysed with semi-automated software to measure cellularity and cell size.ResultsVERDICT parameters were successfully calculated in all patients. The imaging-derived results showed a larger intracellular volume fraction in high-grade glioma compared to low-grade glioma (0.13 ± 0.07 vs. 0.08 ± 0.02, respectively; p = 0.05) and a trend towards a smaller extracellular/vascular volume fraction (0.88 ± 0.07 vs. 0.92 ± 0.04, respectively; p = 0.10). The conventional apparent diffusion coefficient was higher in low-grade gliomas compared to high-grade gliomas, but this difference was not statistically significant (1.22 ± 0.13 × 10-3 mm2/s vs. 0.98 ± 0.38 × 10-3 mm2/s, respectively; p = 0.18).ConclusionThis feasibility study demonstrated that VERDICT MRI can be used to explore the tissue microstructure of glioma using an abbreviated protocol. The VERDICT parameters of tissue structure correlated with those derived on histology. The method shows promise as a potential test for diagnostic stratification and treatment response monitoring in the future.Key points• VERDICT MRI is an advanced diffusion technique which has been correlated with histopathological findings obtained at surgery from patients with glioma in this study. • The intracellular volume fraction measured with VERDICT was larger in high-grade tumours compared to that in low-grade tumours. • The results were complementary to measurements from conventional diffusion-weighted imaging, and the technique could be performed in a clinically feasible timescale.

Project description:Clustering time activity curves of PET images have been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multiorgan level is much less studied due to the available total-body data being limited to animal studies. Now, the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ-level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that are independent of external data and can be used for all tracers, organisms, and health conditions. Additional anatomical image modalities, such as CT or MRI, are not used, but the segmentation is done purely based on the dynamic PET images. The tested methods are commonly used building blocks of the more sophisticated methods rather than final methods as such, and our goal is to evaluate if these basic tools are suited for the arising human total-body PET image segmentation. First, we excluded methods that were computationally too demanding for the large datasets from human total-body PET scanners. These criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k-means and Gaussian mixture model (GMM), for further analyses. We combined k-means with two different preprocessing approaches, namely, principal component analysis (PCA) and independent component analysis (ICA). Then, we selected a suitable number of clusters using 10 images. Finally, we tested how well the usable approaches segment the remaining PET images in organ level, highlight the best approaches together with their limitations, and discuss how further research could tackle the observed shortcomings. In this study, we utilised 40 total-body [18F] fluorodeoxyglucose PET images of rats to mimic the coming large human PET images and a few actual human total-body images to ensure that our conclusions from the rat data generalise to the human data. Our results show that ICA combined with k-means has weaker performance than the other two computationally usable approaches and that certain organs are easier to segment than others. While GMM performed sufficiently, it was by far the slowest one among the tested approaches, making k-means combined with PCA the most promising candidate for further development. However, even with the best methods, the mean Jaccard index was slightly below 0.5 for the easiest tested organ and below 0.2 for the most challenging organ. Thus, we conclude that there is a lack of accurate and computationally light general-purpose segmentation method that can analyse dynamic total-body PET images.

Dataset Information

Microvascular MRI and unsupervised clustering yields histology-resembling images in two rat models of glioma.

Publications

Microvascular MRI and unsupervised clustering yields histology-resembling images in two rat models of glioma.

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