Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities
Martha Foltyn‐DumitruTobias KeßlerFelix SahmWolfgang WickSabine HeilandMartin BendszusPhilipp KickingerederMarianne Schell
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Abstract Background While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. Methods A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. Results Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each). Conclusions Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.Keywords:
Hierarchical clustering
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Abstract This review covers the fundamentals of diffusion tensor imaging. It is written with the clinician in mind and assumes the reader has a passing familiarity with magnetic resonance imaging (MRI). Topics covered include comparison of diffusion MRI with conventional MRI, water apparent diffusion coefficient (ADC), diffusion anisotropy, tract tracing, and changes of water apparent diffusion in response to injury. The discussion centers primarily on applications to the central nervous system, but examples from other tissues are included. J. Magn. Reson. Imaging 2007. © 2007 Wiley‐Liss, Inc.
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Although the free water content within the perilesional T2 hyperintense region should differ between glioblastomas (GBM) and brain metastases based on histological differences, the application of classical MR diffusion models has led to inconsistent results regarding the differentiation between these two entities. Whereas diffusion tensor imaging (DTI) considers the voxel as a single compartment, multicompartment approaches such as neurite orientation dispersion and density imaging (NODDI) or the recently introduced diffusion microstructure imaging (DMI) allow for the calculation of the relative proportions of intra- and extra-axonal and also free water compartments in brain tissue. We investigate the potential of water-sensitive DTI, NODDI and DMI metrics to detect differences in free water content of the perilesional T2 hyperintense area between histopathologically confirmed GBM and brain metastases. Respective diffusion metrics most susceptible to alterations in the free water content (MD, V-ISO, V-CSF) were extracted from T2 hyperintense perilesional areas, normalized and compared in 24 patients with GBM and 25 with brain metastases. DTI MD was significantly increased in metastases (p = 0.006) compared to GBM, which was corroborated by an increased DMI V-CSF (p = 0.001), while the NODDI-derived ISO-VF showed only trend level increase in metastases not reaching significance (p = 0.060). In conclusion, diffusion MRI metrics are able to detect subtle differences in the free water content of perilesional T2 hyperintense areas in GBM and metastases, whereas DMI seems to be superior to DTI and NODDI.
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Diffusion imaging is an MRI modality that measures the microscopic molecular motion of water in order to investigate white matter microstructure. The modality has been used extensively in recent years to investigate the neuroanatomical basis of congenital brain malformations. We review the basic principles of diffusion imaging and of specific techniques, including diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI). We show how DTI and HARDI, and their application to fiber tractography, has elucidated the aberrant connectivity underlying a number of congenital brain malformations. Finally, we discuss potential uses for diffusion imaging of developmental disorders in the clinical and research realms.
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Diffusion imaging is a quantitative, MR-based technique potentially useful for the study of multiple sclerosis (MS), due to its increased pathologic specificity over conventional MRI and its ability to assess in vivo the presence of tissue damage occurring outside T2-visible lesions, i.e., in the so-called normal-appearing white and gray matter. The present review aims at critically summarizing the state-of-the-art and providing a background for the planning of future diffusion studies of MS. Several pieces of evidence suggest that diffusion-weighted and diffusion tensor MRI are sensitive to MS damage and able to detect its evolution over relatively short periods of time. Although a significant relationship between diffusion-weighted MRI findings and MS clinical disability was not found in the earliest studies, with improved diffusion imaging technology correlations between diffusion abnormalities and MS clinical aspects are now emerging. However, the best acquisition and postprocessing strategies for MS studies remain a matter of debate and the contribution of newer and more sophisticated techniques to diffusion tensor MRI investigations in MS needs to be further evaluated. Although changes in diffusion MRI indices reflect a net loss of structural organization, at present we can only speculate on their possible pathologic substrates in the MS brain. Postmortem studies correlating diffusion findings with histopathology of patients with MS are, therefore, also warranted.
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This paper reviews the use of magnetic resonance diffusion imaging in studies of multiple sclerosis. Firstly, the principles of diffusion imaging are explained together with a discussion of the hardware and techniques required. The concept of diffusion tensor imaging is introduced and images obtained using this method are presented. Studies that have used diffusion imaging in patients with multiple sclerosis and the implications of the results are discussed. There is an increase in the diffusion coefficient of water molecules in the plaques of patients with multiple sclerosis, compared with healthy brain. Some workers also report increased diffusion in the normal appearing white matter of some patients with multiple sclerosis. Possible mechanisms are given for these findings, together with the experimental evidence to support them.
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Although some diffusion-weighted imaging (DWI) techniques have entered the stage of clinical routine application, particularly in the detection of cerebral infarction, obtaining and interpreting diffusion imaging results is not always straightforward. This reflects both the numerous technical difficulties and also the sensitivity of diffusion imaging experiments to phenomena other than diffusion.[1,2] Further complications arise from the sheer number of diffusion parameters that can be derived from the measurement in biological tissue, such as eigenvectors, eigenvalues, anisotropy, and trace of the diffusion tensor, diffusion coefficients for a given direction, and so on. This chapter aims at providing an overview of the most important difficulties encountered in MR diffusion imaging.
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Diffusion-tensor imaging is an emerging technique that can supply microscopic structural information about tissue in vivo. With this technique it is possible to measure the amount of anisotropy of water diffusion within tissues and to assess the degree to which directionally ordered tissues have lost their normal integrity. This study was performed in four patients to evaluate the feasibility of applying this technique in clinical situations in which there is known or suspected damage to white matter tracts.
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