The wavelet power spectrum of perfusion weighted MRI correlates with tumor vascularity in biopsy-proven glioblastoma samples

Rotkopf, Lukas T.; Wiestler, Benedikt; Preibisch, Christine; Liesche‐Starnecker, Friederike; Pyka, Thomas; Nörenberg, Dominik; Bette, Stefanie; Gempt, Jens; Thierfelder, Kolja M.; Zimmer, Claus; Huber, Thomas S.

doi:10.1371/journal.pone.0228030

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…In the 10/17 patients who underwent needle biopsy, the stereotactic coordinates of the sampling sites were imported in the 3D Slicer Dataset as a 5 mm diameter spherical volume, similarly to what was previously described by other authors ( Jun et al, 2006 ; Barajas R. F. et al, 2012 ; Gates et al, 2019 ; Rotkopf et al, 2020 ), in order to take into account the maximum reported accuracy error in Euclidean distance reported in the literature ( Roth et al, 2018 ). Each histopathological specimen, labeled according to the order of resection, was then analyzed in comparison to the corresponding voxels and habitats in the 3D Slicer Dataset.…”

Section: Methodsmentioning

confidence: 99%

Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study

et al. 2022

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BackgroundTumor heterogeneity poses major clinical challenges in high-grade gliomas (HGGs). Quantitative radiomic analysis with spatial tumor habitat clustering represents an innovative, non-invasive approach to represent and quantify tumor microenvironment heterogeneity. To date, habitat imaging has been applied mainly on conventional magnetic resonance imaging (MRI), although virtually extendible to any imaging modality, including advanced MRI techniques such as perfusion and diffusion MRI as well as positron emission tomography (PET) imaging.ObjectivesThis study aims to evaluate an innovative PET and MRI approach for assessing hypoxia, perfusion, and tissue diffusion in HGGs and derive a combined map for clustering of intra-tumor heterogeneity.Materials and MethodsSeventeen patients harboring HGGs underwent a pre-operative acquisition of MR perfusion (PWI), Diffusion (dMRI) and 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET imaging to evaluate tumor vascularization, cellularity, and hypoxia, respectively. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast images, and voxel-wise clustering of each quantitative imaging map identified eight combined PET and physiologic MRI habitats. Habitats’ spatial distribution, quantitative features and histopathological characteristics were analyzed.ResultsA highly reproducible distribution pattern of the clusters was observed among different cases, particularly with respect to morphological landmarks as the necrotic core, contrast-enhancing vital tumor, and peritumoral infiltration and edema, providing valuable supplementary information to conventional imaging. A preliminary analysis, performed on stereotactic bioptic samples where exact intracranial coordinates were available, identified a reliable correlation between the expected microenvironment of the different spatial habitats and the actual histopathological features. A trend toward a higher representation of the most aggressive clusters in WHO (World Health Organization) grade IV compared to WHO III was observed.ConclusionPreliminary findings demonstrated high reproducibility of the PET and MRI hypoxia, perfusion, and tissue diffusion spatial habitat maps and correlation with disease-specific histopathological features.

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Section: Methodsmentioning

confidence: 99%

Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study

et al. 2022

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“…Ki67 is a marker of cell proliferation; higher values are associated with a higher proliferation index and consequently a higher tumor grade. A high proliferation index was related to vascularization and to a higher relative cerebral blood volume (rCBV) in glioblastoma ( 13 , 168 ), and an inverse correlation was seen between the Ki-67 proliferation index and the apparent diffusion coefficient (ADC) across glioma grades ( 13 ). Positive correlations between the Ki-67 proliferation index and metabolite alterations of choline over creatine ratio, lactate over creatine ratio, and myo-inositol were observed with MRS ( 169 ).…”

Section: Non-invasive Diagnosis Of Gliomasmentioning

confidence: 99%

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

Balañá

Castañer²,

Carrato

et al. 2022

Front. Neurol.

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Gliomas are a heterogenous group of central nervous system tumors with different outcomes and different therapeutic needs. Glioblastoma, the most common subtype in adults, has a very poor prognosis and disabling consequences. The World Health Organization (WHO) classification specifies that the typing and grading of gliomas should include molecular markers. The molecular characterization of gliomas has implications for prognosis, treatment planning, and prediction of treatment response. At present, gliomas are diagnosed via tumor resection or biopsy, which are always invasive and frequently risky methods. In recent years, however, substantial advances have been made in developing different methods for the molecular characterization of tumors through the analysis of products shed in body fluids. Known as liquid biopsies, these analyses can potentially provide diagnostic and prognostic information, guidance on choice of treatment, and real-time information on tumor status. In addition, magnetic resonance imaging (MRI) is another good source of tumor data; radiomics and radiogenomics can link the imaging phenotypes to gene expression patterns and provide insights to tumor biology and underlying molecular signatures. Machine and deep learning and computational techniques can also use quantitative imaging features to non-invasively detect genetic mutations. The key molecular information obtained with liquid biopsies and radiogenomics can be useful not only in the diagnosis of gliomas but can also help predict response to specific treatments and provide guidelines for personalized medicine. In this article, we review the available data on the molecular characterization of gliomas using the non-invasive methods of liquid biopsy and MRI and suggest that these tools could be used in the future for the preoperative diagnosis of gliomas.

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“…1B. While the calculation of conventional parameters like CBF, CBV, time to peak (TTP), and mean transit time (MTT) is well-established and a mainstay of neurooncological and neurovascular diagnosis, novel processing methods like wavelet-based analysis [22,23], Bayesian vascular models [24], and control-point interpolation methods [25] may be able to capture pathological changes better. In addition, direct inference from the tissue residuum function may be able to capture important features which are missed by calculated single parameters.…”

Section: Perfusion Modelingmentioning

confidence: 99%

Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning

Rotkopf

Zhang

Tavakoli

et al. 2022

Rofo

Self Cite

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Background Perfusion MRI is a well-established imaging modality with a multitude of applications in oncological and cardiovascular imaging. Clinically used processing methods, while stable and robust, have remained largely unchanged in recent years. Despite promising results from novel methods, their relatively minimal improvement compared to established methods did not generally warrant significant changes to clinical perfusion processing. Results and Conclusion Machine learning in general and deep learning in particular, which are currently revolutionizing computer-aided diagnosis, may carry the potential to change this situation and truly capture the potential of perfusion imaging. Recent advances in the training of recurrent neural networks make it possible to predict and classify time series data with high accuracy. Combining physics-based tissue models and deep learning, using either physics-informed neural networks or universal differential equations, simplifies the training process and increases the interpretability of the resulting models. Due to their versatility, these methods will potentially be useful in bridging the gap between microvascular architecture and perfusion parameters, akin to MR fingerprinting in structural MR imaging. Still, further research is urgently needed before these methods may be used in clinical practice. Key Points: Citation Format

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The wavelet power spectrum of perfusion weighted MRI correlates with tumor vascularity in biopsy-proven glioblastoma samples

Cited by 5 publications

References 32 publications

Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study

Decoding the Heterogeneity of Malignant Gliomas by PET and MRI for Spatial Habitat Analysis of Hypoxia, Perfusion, and Diffusion Imaging: A Preliminary Study

Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics

Quantitative Analysis of DCE and DSC-MRI: From Kinetic Modeling to Deep Learning

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