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Quantification of Radiomics features of peritumoral vasogenic edema extracted from FLAIR images in glioblastoma and isolated brain metastasis, using T1‐DCE perfusion analysis
Abstract
The peritumoral vasogenic edema (PVE) in brain tumors exhibits varied characteristics. Brain metastasis (BM) and meningioma barely have tumor cells in PVE, while Glioblastoma (GB) show tumor cell infiltration in most subjects. The purpose of this study was to investigate the PVE of these three pathologies using Radiomics features in FLAIR images, with the hypothesis that the tumor cells might influence textural variation. Ex‐vivo experimentation of Radiomics analysis of T1‐weighted images of the culture medium with and without suspended tumor cells was also attempted to infer the possible influence of increasing tumor cells on Radiomics features. This retrospective study involved MR images acquired using 3.0T MR machine from 83 patients having 48 GB, 21 BM and 14 Meningioma. The 93 Radiomics features were extracted from each subject’s PVE mask from 3 pathologies using T1‐DCE MRI. Statistically significant (<0.05, independent samples T‐test) features were considered. Features maps were also computed for qualitative investigation. The same was carried out for T1‐weighted cell line images but group comparison was carried using One‐way ANOVA. Further, a random forest (RF) based machine learning (ML) model was designed to classify the PVE of GB and BM. The texture‐based variation especially higher non‐uniformity values were observed in the PVE of GB. No significance was observed between BM and Meningioma PVE. In cell line images, the culture medium had higher non‐uniformity and considerably reduced with increasing cell densities in 4 features. RF model implemented with highly significant features provided improved AUC results. The possible infiltrative tumor cells in the PVE of the GB are likely influencing the texture values and are higher in comparison with BM PVE and may be of value in differentiation of solitary metastasis from GB. However, the robustness of the features needs to be investigated with larger cohort and across the scanners in future.
... Due to differences in clinical treatment and prognosis, it is critical to distinguish between primary central nervous system lymphoma (PCNSL), high-grade gliomas (HGGs), and metastatic glioma (72)(73)(74)(75)(76)(77)(78)(79) ...
Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix’s conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.
... Their network achieved 92.4% for accuracy, 88.9% for precision, and 80% for sensitivity. P. Suhail et al. [37] used radiomics features to examine the peritumoral vasogenic edema (PVE) of Brain metastasis, meningioma, and glioblastoma in FLAIR images. Moreover, they developed a random forest (RF)-based machine learning model to classify the PVE of glioblastoma and Brain metastasis. ...
The COVID-19 worldwide pandemic has become a great challenge for medical systems. Early detection of coronavirus increases the cure rate and saves the lives of patients. Therefore, a computer-assisted diagnostic tool is necessary to assist radiologists to quickly classify cases of pneumonia as COVID-19 or not. In this paper, we developed an approach that automatically detects COVID-19 disease. This approach relies on an optimized convolutional neural networks (CNNs) to extract deep features from both the computed tomography (CT) scans and the X-ray images. Then, features selection process is applied on CT and X-ray features to eliminate the redundant and irrelevant features and also select more accurate, appropriate, and discriminative ones. The selected CT and X-ray features are combined together using features fusion method to form a final deep feature descriptor for classification. CT and X-ray features are extracted using an optimized CNN architecture model containing thirteen convolutional layers. Sparse Filtering (SF), Features Transformation (FT), Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Hybrid Whale Optimization (HWO), and Entropy Mutual Information (EMI) Techniques are utilized for features selection. Furthermore, various features fusion algorithms are evaluated such as; CNN-based Fusion, Fuzzy Fusion, Canonical Correlation, Neuro Fuzzy, Curvelet based Fusion, and Discrete Wavelet Transform (DWT). Besides, for both augmented and un-augmented CT and X-ray images, we evaluate various optimization algorithms (OA), mini-batch size (M-B) values, and learning rates (LR) to reveal the optimal parameters of the proposed CNN architecture model. Regarding optimization, adaptive moment estimation (Adam) algorithm showed better performance than root mean square propagation (RMS prop), and stochastic gradient descent with momentum (SGDM). The proposed approach achieves a remarkable accuracy (99.43%) for augmented images using Hybrid Whale Optimization (HWO) technique with Canonical Correlation based fusion approach. The proposed approach is superior to traditional pre-trained CNNs and the state-of-the-art classification techniques.
... Moreover, Wang et al. fused individual features from both CNN and GCN to assist radiologists in rapidly detecting COVID-19 from chest CT images [54]. Parvaze et al. extracting crafted features to analyze and identification the pathologies features of peritumoral vasogenic edema [35,36]. ...
Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) were two most common chronic middle ear disease(MED) clinically. Accurate differential diagnosis between these two diseases is of high clinical importance given the difference in etiologies, lesion manifestations and treatments. The high-resolution computed tomography (CT) scanning of the temporal bone presents a better view of auditory structures, which is currently regarded as the first-line diagnostic imaging modality in the case of MED. In this paper, we first used a region-of-interest (ROI) network to find the area of the middle ear in the entire temporal bone CT image and segment it to a size of 100*100 pixels. Then, we used a structure-constrained deep feature fusion algorithm to convert different characteristic features of the middle ear in three groups as suppurative otitis media (CSOM), middle ear cholesteatoma (MEC) and normal patches. To fuse structure information, we introduced a graph isomorphism network that implements a feature vector from neighbourhoods and the coordinate distance between vertices. Finally, we construct a classifier named the “otitis media, cholesteatoma and normal identification classifier” (OMCNIC). The experimental results achieved by the graph isomorphism network revealed a 96.36% accuracy in all CSOM and MEC classifications. The experimental results indicate that our structure-constrained deep feature fusion algorithm can quickly and effectively classify CSOM and MEC. It will help otologist in the selection of the most appropriate treatment, and the complications can also be reduced.
Angiogenesis is a key step in the initiation and progression of an invasive breast cancer. High microvessel density by morphological characterization predicts metastasis and poor survival in women with invasive breast cancers. However, morphologic characterization is subject to variability and only can evaluate a limited portion of an invasive breast cancer. Consequently, breast Magnetic Resonance Imaging (MRI) is currently being evaluated to assess vascularity. Recently, through the new field of radiomics, dynamic contrast enhanced (DCE)-MRI is being used to evaluate vascular density, vascular morphology, and detection of aggressive breast cancer biology. While DCE-MRI is a highly sensitive tool, there are specific features that limit computational evaluation of blood vessels. These include (1) DCE-MRI evaluates gadolinium contrast and does not directly evaluate biology, (2) the resolution of DCE-MRI is insufficient for imaging small blood vessels, and (3) DCE-MRI images are very difficult to co-register. Here we review computational approaches for detection and analysis of blood vessels in DCE-MRI images and present some of the strategies we have developed for co-registry of DCE-MRI images and early detection of vascularization.
Simple Summary
Glioblastomas (GBMs) are the most frequent and aggressive malignant tumors arising in the human brain. One of the main reasons for GBM aggressiveness is its diverse cellular composition, comprised by differentiated tumor cells, tumor stem cells, cells from the blood vessels, and inflammatory cells, which simultaneously affect multiple cellular functions involved in cancer development. “Tumor Heterogeneity” usually encompasses both inter-tumor heterogeneity, differences observed at population level; and intra-tumor heterogeneity, differences among cells within individual tumors, which directly affect outcomes and response to treatment. In this review, we briefly describe the evolution of GBM classification yielded from inter-tumor heterogeneity studies and discuss how the technological development allows for the characterization of intra-tumor heterogeneity, beginning with differences based on histopathological features of GBM until the molecular alterations in DNA, RNA, and proteins observed at individual cells.
Abstract
One of the main reasons for the aggressive behavior of glioblastoma (GBM) is its intrinsic intra-tumor heterogeneity, characterized by the presence of clonal and subclonal differentiated tumor cell populations, glioma stem cells, and components of the tumor microenvironment, which affect multiple hallmark cellular functions in cancer. “Tumor Heterogeneity” usually encompasses both inter-tumor heterogeneity (population-level differences); and intra-tumor heterogeneity (differences within individual tumors). Tumor heterogeneity may be assessed in a single time point (spatial heterogeneity) or along the clinical evolution of GBM (longitudinal heterogeneity). Molecular methods may detect clonal and subclonal alterations to describe tumor evolution, even when samples from multiple areas are collected in the same time point (spatial-temporal heterogeneity). In GBM, although the inter-tumor mutational landscape is relatively homogeneous, intra-tumor heterogeneity is a striking feature of this tumor. In this review, we will address briefly the inter-tumor heterogeneity of the CNS tumors that yielded the current glioma classification. Next, we will take a deeper dive in the intra-tumor heterogeneity of GBMs, which directly affects prognosis and response to treatment. Our approach aims to follow technological developments, allowing for characterization of intra-tumor heterogeneity, beginning with differences on histomorphology of GBM and ending with molecular alterations observed at single-cell level.
Background:
The purpose of this study was to investigate the value of wavelet-transformed radiomic MRI in predicting the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) for patients with locally advanced breast cancer (LABC).
Methods:
Fifty-five female patients with LABC who underwent contrast-enhanced MRI (CE-MRI) examination prior to NAC were collected for the retrospective study. According to the pathological assessment after NAC, patient responses to NAC were categorized into pCR and non-pCR. Three groups of radiomic textures were calculated in the segmented lesions, including (1) volumetric textures, (2) peripheral textures, and (3) wavelet-transformed textures. Six models for the prediction of pCR were Model I: group (1), Model II: group (1) + (2), Model III: group (3), Model IV: group (1) + (3), Model V: group (2) + (3), and Model VI: group (1) + (2) + (3). The performance of predicting models was compared using the area under the receiver operating characteristic (ROC) curves (AUC).
Results:
The AUCs of the six models for the prediction of pCR were 0.816 ± 0.033 (Model I), 0.823 ± 0.020 (Model II), 0.888 ± 0.025 (Model III), 0.876 ± 0.015 (Model IV), 0.885 ± 0.030 (Model V), and 0.874 ± 0.019 (Model VI). The performance of four models with wavelet-transformed textures (Models III, IV, V, and VI) was significantly better than those without wavelet-transformed textures (Model I and II). In addition, the inclusion of volumetric textures or peripheral textures or both did not result in any improvements in performance.
Conclusions:
Wavelet-transformed textures outperformed volumetric and/or peripheral textures in the radiomic MRI prediction of pCR to NAC for patients with LABC, which can potentially serve as a surrogate biomarker for the prediction of the response of LABC to NAC.
Brain, the major organ of the central nervous system controls and processes most of body activities. Therefore, the most aggressive brain tumor – glioblastoma and metastases from other organs to the brain are lethal leaving the patients with very short time of survival. The brain tissue landscape is very different from any other tissues and the specific microenvironment, comprising stem cells niches and blood-brain barrier, significantly influences the low rate of glioblastoma metastasis out of the brain, but better accommodates brain-invading cancer. In contrast to low frequency (0.5%) of all glioblastoma metastases, 10%–45% of other primary cancers do metastasize to the brain. This review addresses general cellular and molecular pathways that are to some extent similar in both types of metastases, involving circulating tumor cells (CTCs) with cancer stem cells (CSCs) characteristics, and metastatic niches. The invasion is a dynamic process involving reversible epithelial-to-mesenchymal (EMT) cell process, creating a transient gradient state that is inter-connected with epigenetic plasticity of the metastasizing (m)CSCs. These cells can switch between stationary, low proliferating/dormant state to a migratory, mesenchymal-like state. Settling in their respective niches as dormant CSCs in the secondary organ is a common feature in all types of metastases. In glioblastoma metastasis, the malignant mGSC cells express markers of mesenchymal GSC subtype (MES-GSC), such as CD44 and YK-40 and their major obstacle seems to be propagating in the in various organs’ microenvironments, different from the niches that home GSCs in the primary glioblastoma. Focusing on one stromal component in the glioblastoma niches, the mesenchymal stem cells (MSCs), we report herein on their differential effects on glioblastoma cells, highly depending on their genetic subtype. On the other hand, in brain metastases, the major hindrance to metastatic progression of mCSCs seem to be crossing the blood-brain-barrier. Novel therapeutic approaches for brain metastases from various cancer types are advancing slowly, and the general trends involve targeting metastatic sub-clones and selective determinants of their niches. The update on the four most common brain metastases from lung, breast, melanoma and colorectal carcinoma is presented.
Brain, the major organ of the central nervous system controls and processes most of our body activities and therefore the most aggressive brain tumor - glioblastoma and metastases from other organs to the bran are lethal, leaving the patients with very low survival time. The brain tissue landscape is very different from any other tissues and the specific microenvironment significantly shape up the glioblastoma local progression and metastasis out of the brain, as well as accommodates metastatic brain-invading cancer cells, mostly from lung, breast, melanoma and colorectal carcinoma. In contrast to low frequency (0.5%) of all glioblastoma metastases, from 10%- 45% of other primary cancers metastasize to the brain. In both cases, hematogenous spread of cancer prevails. This review is addressing cellular and molecular pathways that are to some extent similar in both types of metastases, involving circulating tumor cells (CTCa) with cancer stem cells (CSCa) characteristics. In glioblastoma, the metastatic CTCa (mCTCs) often express markers of mesenchymal glioblastoma stem cell subtype (MES-GSC), e.g. CD44 and YK-40, and the problems in their pathway to lymph nodes and other tissues seem to be related to finding the metastatic niches, which might be similar to the niches that home GSC in the primary glioblastoma. In brain metastases, the mCTCs seem to be hindered by normal blood brain barrier (BBB), as very low number of CTCs can extravasate to the brain. Settling in their respective niches in the secondary organ is a common feature of all types of metastases. This is a dynamic process involving reversible EMT-MET cell processes, creating a transient gradient state that is inter-connected with epigenetic plasticity of metastasizing CSCs. These switch between stationary, low proliferating /dormant state to migratory, mesenchymal-like CSC state. This process is extensively described for glioblastoma, where strong dependence on the microenvironmental factors, such as mesenchymal stem cells (MSCs), play a decisive role in GSCs, depending on the GB genetic subtypes. Nonetheless, novel therapeutic approaches for glioblastoma, and in particular, brain metastases and GB metastases, have advanced slowly, and the general trends involve targeting metastatic subclones and selective determinants of their niches.
Purpose: To investigative the diagnostic performance of radiomics-based machine learning in differentiating glioblastomas (GBM) from metastatic brain tumors (MBTs).
Method: The current study involved 134 patients diagnosed and treated in our institution between April 2014 and December 2018. Radiomics features were extracted from contrast-enhanced T1 weighted imaging (T1C). Thirty diagnostic models were built based on five selection methods and six classification algorithms. The sensitivity, specificity, accuracy, and area under curve (AUC) of each model were calculated, and based on these the optimal model was chosen.
Result : Two models represented promising diagnostic performance with AUC of 0.80. The first model was a combination of Distance Correlation as the selection method and Linear Discriminant Analysis (LDA) as the classification algorithm. In the training group, the sensitivity, specificity, accuracy, and AUC were 0.75, 0.85, 0.80, and 0.80, respectively; and in the testing group, the sensitivity, specificity, accuracy, and AUC of the model were 0.69, 0.86, 0.78, and 0.80, respectively. The second model was the Distance Correlation as the selection method and logistic regression (LR) as the classification algorithm, with sensitivity, specificity, accuracy, and AUC of 0.75, 0.85, 0.80, 0.80 in the training group and 0.69, 0.86, 0.78, 0.80 in the testing group.
Conclusion: Radiomic-based machine learning has potential to be utilized in differentiating GBM from MBTs.
Quantitative imaging features (radiomics) extracted from apparent diffusion coefficient (ADC) maps of rectal cancer patients can provide additional information to support treatment decision. Most available radiomic computational packages allow extraction of hundreds to thousands of features. However, two major factors can influence the reproducibility of radiomic features: interobserver variability, and imaging filtering applied prior to features extraction. In this exploratory study we seek to determine to what extent various commonly-used features are reproducible with regards to the mentioned factors using ADC maps from two different clinics (56 patients). Features derived from intensity distribution histograms are less sensitive to manual tumour delineation differences, noise in ADC images, pixel size resampling and intensity discretization. Shape features appear to be strongly affected by delineation quality. On the whole, textural features appear to be poorly or moderately reproducible with respect to the image pre-processing perturbations we reproduced.
Purpose
We propose a novel methodology to integrate morphological and functional information of tumor‐associated vessels to assist in the diagnosis of suspicious breast lesions.
Theory and Methods
Ultrafast, fast, and high spatial resolution DCE‐MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE‐MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, Ktrans (volume transfer coefficient), and vp (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability.
Results
A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, Ktrans, and vp showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91).
Conclusion
This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.
Hypoxia, a characteristic trait of Glioblastoma (GBM), is known to cause resistance to chemo-radiation treatment and is linked with poor survival. There is hence an urgent need to non-invasively characterize tumor hypoxia to improve GBM management. We hypothesized that (a) radiomic texture descriptors can capture tumor heterogeneity manifested as a result of molecular variations in tumor hypoxia, on routine treatment naïve MRI, and (b) these imaging based texture surrogate markers of hypoxia can discriminate GBM patients as short-term (STS), mid-term (MTS), and long-term survivors (LTS). 115 studies (33 STS, 41 MTS, 41 LTS) with gadolinium-enhanced T1-weighted MRI (Gd-T1w) and T2-weighted (T2w) and FLAIR MRI protocols and the corresponding RNA sequences were obtained. After expert segmentation of necrotic, enhancing, and edematous/nonenhancing tumor regions for every study, 30 radiomic texture descriptors were extracted from every region across every MRI protocol. Using the expression profile of 21 hypoxia-associated genes, a hypoxia enrichment score (HES) was obtained for the training cohort of 85 cases. Mutual information score was used to identify a subset of radiomic features that were most informative of HES within 3-fold cross-validation to categorize studies as STS, MTS, and LTS. When validated on an additional cohort of 30 studies (11 STS, 9 MTS, 10 LTS), our results revealed that the most discriminative features of HES were also able to distinguish STS from LTS (p = 0.003).
Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7. ©2017 AACR.
Background
The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Methods
Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. ResultsThe idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a “layered diagnosis” to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an “integrated diagnosis” at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only established in a small number of institutions. Conclusion
In summary, while neuropathologists have now encountered various challenges in the transitional phase from the previous WHO 2007 version to the new WHO 2016 classification of brain tumors, clinical neurooncologists now face many new diagnoses allowing a clearly improved understanding that could offer them more effective therapeutic opportunities in neurooncological treatment. The new WHO 2016 classification presumably presents the highest number of modifications since the initial WHO classification of 1979 and thereby forces all professionals in the field of neurooncology to intensively understand the new concepts. This review article aims to present the basic concepts of the new WHO 2016 brain tumor classification for neurosurgeons with a focus on neurooncology.
Cerebral edema associated with brain tumors is extremely common and can occur in both primary and metastatic tumors. The edema surrounding brain tumors results from leakage of plasma across the vessel wall into the parenchyma secondary to disruption of the blood-brain barrier. The clinical signs of brain tumor edema depend on the location of the tumor as well as the extent of the edema, which often exceeds the mass effect induced by the tumor itself. Uncontrolled cerebral edema may result in increased intracranial pressure and acute herniation syndromes that can result in permanent neurological dysfunction and potentially fatal herniation. Treatment strategies for elevated intracranial pressure consist of general measures, medical interventions, and surgery. Alhough the definitive treatment for the edema may ultimately be surgical resection of the tumor, the impact of the critical care management cannot be underestimated and thus patients must be vigilantly monitored in the intensive care unit. In this review, we discuss the pathology, pathophysiology, and clinical features of patients presenting with cerebral edema. Imaging findings and treatment modalities used in the intensive care unit are also discussed.
Introduction:
Perfusion magnetic resonance imaging (MRI) can be used in the pre-operative assessment of brain tumours. The aim of this prospective study was to identify the perfusion parameters from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) perfusion imaging that could best discriminate between grade II and III gliomas.
Methods:
MRI (3 T) including morphological ((T2 fluid attenuated inversion recovery (FLAIR) and T1-weighted (T1W)+Gd)) and perfusion (DCE and DSC) sequences was performed in 39 patients with newly diagnosed suspected low-grade glioma after written informed consent in this review board-approved study. Regions of interests (ROIs) in tumour area were delineated on FLAIR images co-registered to DCE and DSC, respectively, in 25 patients with histopathological grade II (n = 18) and III (n = 7) gliomas. Statistical analysis of differences between grade II and grade III gliomas in histogram perfusion parameters was performed, and the areas under the curves (AUC) from the ROC analyses were evaluated.
Results:
In DCE, the skewness of transfer constant (k(trans)) was found superior for differentiating grade II from grade III in all gliomas (AUC 0.76). In DSC, the standard deviation of relative cerebral blood flow (rCBF) was found superior for differentiating grade II from grade III gliomas (AUC 0.80).
Conclusions:
Histogram parameters from k(trans) (DCE) and rCBF (DSC) could most efficiently discriminate between grade II and grade III gliomas.
Level set methods have been widely used in image processing and computer vision. In conventional level set formulations, the level set function typically develops irregularities during its evolution, which may cause numerical errors and eventually destroy the stability of the evolution. Therefore, a numerical remedy, called reinitialization, is typically applied to periodically replace the degraded level set function with a signed distance function. However, the practice of reinitialization not only raises serious problems as when and how it should be performed, but also affects numerical accuracy in an undesirable way. This paper proposes a new variational level set formulation in which the regularity of the level set function is intrinsically maintained during the level set evolution. The level set evolution is derived as the gradient flow that minimizes an energy functional with a distance regularization term and an external energy that drives the motion of the zero level set toward desired locations. The distance regularization term is defined with a potential function such that the derived level set evolution has a unique forward-and-backward (FAB) diffusion effect, which is able to maintain a desired shape of the level set function, particularly a signed distance profile near the zero level set. This yields a new type of level set evolution called distance regularized level set evolution (DRLSE). The distance regularization effect eliminates the need for reinitialization and thereby avoids its induced numerical errors. In contrast to complicated implementations of conventional level set formulations, a simpler and more efficient finite difference scheme can be used to implement the DRLSE formulation. DRLSE also allows the use of more general and efficient initialization of the level set function. In its numerical implementation, relatively large time steps can be used in the finite difference scheme to reduce the number of iterations, while ensuring sufficient-
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numerical accuracy. To demonstrate the effectiveness of the DRLSE formulation, we apply it to an edge-based active contour model for image segmentation, and provide a simple narrowband implementation to greatly reduce computational cost.
An approach to the construction of classifiers from imbalanced datasets is
described. A dataset is imbalanced if the classification categories are not
approximately equally represented. Often real-world data sets are predominately
composed of "normal" examples with only a small percentage of "abnormal" or
"interesting" examples. It is also the case that the cost of misclassifying an
abnormal (interesting) example as a normal example is often much higher than
the cost of the reverse error. Under-sampling of the majority (normal) class
has been proposed as a good means of increasing the sensitivity of a classifier
to the minority class. This paper shows that a combination of our method of
over-sampling the minority (abnormal) class and under-sampling the majority
(normal) class can achieve better classifier performance (in ROC space) than
only under-sampling the majority class. This paper also shows that a
combination of our method of over-sampling the minority class and
under-sampling the majority class can achieve better classifier performance (in
ROC space) than varying the loss ratios in Ripper or class priors in Naive
Bayes. Our method of over-sampling the minority class involves creating
synthetic minority class examples. Experiments are performed using C4.5, Ripper
and a Naive Bayes classifier. The method is evaluated using the area under the
Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
To determine the potential of using a computer-aided detection method to intelligently distinguish peritumoral edema alone from peritumor edema consisting of tumor using a combination of high-resolution morphological and physiological magnetic resonance imaging (MRI) techniques available on most clinical MRI scanners.
This retrospective study consisted of patients with two types of primary brain tumors: meningiomas (n = 7) and glioblastomas (n = 11). Meningiomas are typically benign and have a clear delineation of tumor and edema. Glioblastomas are known to invade outside the contrast-enhancing area. Four classifiers of differing designs were trained using morphological, diffusion-weighted, and perfusion-weighted features derived from MRI to discriminate tumor and edema, tested on edematous regions surrounding tumors, and assessed for their ability to detect nonenhancing tumor invasion.
The four classifiers provided similar measures of accuracy when applied to the training and testing data. Each classifier was able to identify areas of nonenhancing tumor invasion supported with adjunct images or follow-up studies.
The combination of features derived from morphological and physiological imaging techniques contains the information necessary for computer-aided detection of tumor invasion and allows for the identification of tumor invasion not previously visualized on morphological, diffusion-weighted, and perfusion-weighted images and maps. Further validation of this approach requires obtaining spatially coregistered tissue samples in a study with a larger sample size.
The efficacy of radiation therapy, the mainstay of treatment for malignant gliomas, is limited by our inability to accurately determine tumor margins. As a result, despite recent advances, the prognosis remains appalling. Because gliomas preferentially infiltrate along white matter tracks, methods that show white matter disruption should improve this delineation. In this study, results of histologic examination from samples obtained from image-guided brain biopsies were correlated with diffusion tensor images.
Twenty patients requiring image-guided biopsies for presumed gliomas were imaged preoperatively. Patients underwent image-guided biopsies with multiple biopsies taken along a single track that went into normal-appearing brain. Regions of interest were determined from the sites of the biopsies, and diffusion tensor imaging findings were compared with glioma histology.
Using diffusion tissue signatures, it was possible to differentiate gross tumor (reduction of the anisotropic component, q > 12% from contralateral region), from tumor infiltration (increase in the isotropic component, p > 10% from contralateral region). This technique has a sensitivity of 98% and specificity of 81%. T2-weighted abnormalities failed to identify the margin in half of all specimens.
Diffusion tensor imaging can better delineate the tumor margin in gliomas. Such techniques can improve the delineation of the radiation therapy target volume for gliomas and potentially can direct local therapies for tumor infiltration.
Radiomics based feature value analysis and feature maps especially for first order and texture based features are being thoroughly investigated for diagnostic usability and relevance. Radiomics based feature map generation using 2D and 3D modes on different tumor sub-regions of glioblastoma were performed. 2D-based feature maps depicted accurately the texture variation with respect to other images from the slice of interest, whereas in 3D based maps anatomy and pathology of the neighboring slices induced influence resulting in over depiction of pathology.
Objective
Differentiating glioblastoma (GBM) and solitary metastasis is not always possible using conventional magnetic resonance imaging (MRI) techniques. In conventional brain MRI, GBM and brain metastases are lesions with mostly similar imaging findings. In this study, we investigated whether apparent diffusion coefficient (ADC) ratios, ADC gradients, and minimum ADC values in the peritumoral edema tissue can be used to discriminate between these two tumors.
Methods
This retrospective study was approved by the local institutional review board with a waiver of written informed consent. Prior to surgical and medical treatment, conventional brain MRI and diffusion-weighted MRI (b = 0 and b = 1000) images were taken from 43 patients (12 GBM and 31 solitary metastasis cases). Quantitative ADC measurements were performed on the peritumoral tissue from the nearest segment to the tumor (ADC1), the middle segment (ADC2), and the most distant segment (ADC3). The ratios of these three values were determined proportionally to calculate the peritumoral ADC ratios. In addition, these three values were subtracted from each other to obtain the peritumoral ADC gradients. Lastly, the minimum peritumoral and tumoral ADC values, and the quantitative ADC values from the normal appearing ipsilateral white matter, contralateral white matter and ADC values from cerebrospinal fluid (CSF) were recorded.
Results
For the differentiation of GBM and solitary metastasis, ADC3 / ADC1 was the most powerful parameter with a sensitivity of 91.7% and specificity of 87.1% at the cut-off value of 1.105 (p < 0.001), followed by ADC3 / ADC2 with a cut-off value of 1.025 (p = 0.001), sensitivity of 91.7%, and specificity of 74.2%. The cut-off, sensitivity and specificity of ADC2 / ADC1 were 1.055 (p = 0.002), 83.3%, and 67.7%, respectively. For ADC3 – ADC1, the cut-off value, sensitivity and specificity were calculated as 150 (p < 0.001), 91.7% and 83.9%, respectively. ADC3 – ADC2 had a cut-off value of 55 (p = 0.001), sensitivity of 91.7%, and specificity of 77.4 whereas ADC2 – ADC1 had a cut-off value of 75 (p = 0.003), sensitivity of 91.7%, and specificity of 61.3%. Among the remaining parameters, only the ADC3 value successfully differentiated between GBM and metastasis (GBM 1802.50 ± 189.74 vs. metastasis 1634.52 ± 212.65, p = 0.022).
Conclusion
The integration of the evaluation of peritumoral ADC ratio and ADC gradient into conventional MR imaging may provide valuable information for differentiating GBM from solitary metastatic lesions.
Purpose
To evaluate the efficacy of optimized T1-Perfusion MRI protocol(protocol-2) with whole brain coverage and improved spatial resolution using Compressed-SENSE(CSENSE) to differentiate high-grade-glioma(HGG) and low-grade-glioma(LGG) and to compare it with the conventional protocol(protocol-1) with partial brain coverage used in our center.
Methods
This study included MRI data from 5 healthy volunteers, a phantom and 126 brain tumor patients. Current study had two parts: To analyze the effect of CSENSE on 3D-T1-weighted(W) fast-field-echo(FFE) images, T1-W, dual-PDT2-W turbo-spin-echo images and T1 maps, and to evaluate the performance of high resolution T1-Perfusion MRI protocol with whole brain coverage optimized using CSENSE. Coefficient-of-Variation(COV), Relative-Percentage-Error(RPE), Normalized-Mean-Squared-Error(NMSE) and qualitative scoring were used for the former study. The performance of tracer-kinetic(Ktrans,ve,vp) and hemodynamic(rCBV,rCBF) parameters computed from both protocols were used to differentiate LGG and HGG.
Results
The image quality of all structural images was found to be of diagnostic quality till R = 4. NMSE in healthy T1-W-FFE images and COV in phantom images increased with-respect-to R and images provided optimum quality till R = 4. Structural images and maps exhibited artifacts from R = 6. All parameters in tumor tissue and hemodynamic parameters in healthy gray matter tissue computed from both protocols were not significantly different. Parameters computed from protocol-2 performed better in terms of glioma grading. For both protocols, rCBF performed least (AUC = 0.759 and 0.851) and combination of all parameters performed best (AUC = 0.890 and 0.964).
Conclusion
CSENSE(R = 4) can be used to improve the resolution and brain coverage for T1-Perfusion analysis used to differentiate gliomas.
There is a growing understanding of the prognostic importance of non-contrast-enhancing tumor in glioblastoma, and recent attempts at more aggressive management of this component using neurosurgical resection and radiosurgery have been shown to prolong survival. Optimizing these therapeutic strategies requires an understanding of the features that can distinguish non-contrast-enhancing tumor from other processes, in particular vasogenic edema; however, the limited and heterogeneous manner in which it has been defined in the literature limits clinical translation. This review covers pertinent literature on our growing understanding of non-contrast-enhancing tumor and focuses on key conventional MR imaging features for improving its delineation. Such features include subtle differences in the degree of FLAIR hyperintensity, gray matter involvement, and focal mass effect. Improved delineation of tumor from edema will facilitate more aggressive management of this component and potentially realize associated survival benefits.
Purpose:
High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T2-W/FLAIR images. Most studies involving differentiation between vasogenic edema and non-enhancing tumor consider radiologist-based tumor delineation as the ground truth. However, analysis by a radiologist can be subjective and there remain both inter- and intra-rater differences. The objective of the current study is to develop a methodology for differentiation between non-enhancing tumor and vasogenic edema in HGG patients based on T1 perfusion MRI parameters, using a ground truth which is independent of a radiologist's manual delineation of the tumor.
Material and methods:
This study included 9 HGG patients with pre- and post-surgery MRI data and 9 metastasis patients with pre-surgery MRI data. MRI data included conventional T1-W, T2-W, and FLAIR images and DCE-MRI dynamic images. In this study, the authors hypothesize that surgeried non-enhancing FLAIR hyperintense tissue, which was obtained using pre- and post-surgery MRI images of glioma patients, should be largely comprised of non-enhancing tumor. Hence this could be used as an alternative ground truth for the non-enhancing tumor region. Histological examination of the resected tissue was done for validation. Vasogenic edema was obtained from the non-enhancing FLAIR hyperintense region of metastasis patients, as they have a clear boundary between enhancing tumor and edema. DCE-MRI data analysis was performed to obtain T1 perfusion MRI parameters. Support Vector Machine (SVM) classification was performed using T1 perfusion MRI parameters to differentiate between non-enhancing tumor and vasogenic edema. Receiver-operating-characteristic (ROC) analysis was done on the results of the SVM classifier. For improved classification accuracy, the SVM output was post-processed via neighborhood smoothing.
Results:
Histology results showed that resected tissue consists largely of tumorous tissue with 7.21 ± 4.05% edema and a small amount of healthy tissue. SVM-based classification provided a misclassification error of 8.4% in differentiation between non-enhancing tumor and vasogenic edema, which was further reduced to 2.4% using neighborhood smoothing.
Conclusion:
The current study proposes a semiautomatic method for segmentation between non-enhancing tumor and vasogenic edema in HGG patients, based on an SVM classifier trained on an alternative ground truth to a radiologist's manual delineation of a tumor. The proposed methodology may prove to be a useful tool for pre- and post-operative evaluation of glioma patients.
Objectives:
To evaluate the diagnostic performance of arterial spin labelling perfusion weighted images (ASL-PWIs) to differentiate primary CNS lymphoma (PCNSL) from glioblastoma (GBM).
Methods:
ASL-PWIs of pathologically confirmed PCNSL (n = 21) or GBM (n = 93) were analysed. For qualitative analysis, tumours were visually scored into five categories based on ASL-CBF maps. For quantitative analysis, normalised CBF values were derived by contralateral grey matter (GM) in intra- and peritumoral areas (nCBFintratumoraland nCBFperitumoral, respectively). Visual scoring scales and quantitative parameters from PCNSL and GBM were compared. In addition, the area under the receiver-operating characteristic (ROC) curve was used to determine the diagnostic accuracy of ASL-PWI for differentiating PCNSL from GBM. Weighted kappa or intraclass correlation coefficients (ICCs) were used to assess reliability between two observers.
Results:
In qualitative analysis, scores 5 (CBFintratumoral>CBFGM, 68.8% [64/93]) and 4 (CBFintratumoral≈ CBFGM, 47.6% [10/21]) were the most frequently reported scores for GBM and PCNSL, respectively. In quantitative analysis, both nCBFintratumoraland nCBFperitumoralin PCNSL were significantly lower than those in the GBM (nCBFintratumoral, 0.89 ± 0.59 [mean and SD] vs. 2.68 ± 1.89, p < 0.001; nCBFperitumoral, 0.17 ± 0.08 vs. 0.45 ± 0.28, p < 0.001). nCBFperitumoraldemonstrated the best diagnostic performance (area under the ROC curve: visual scoring, 0.814; nCBFintratumoral, 0.849; nCBFperitumoral, 0.908; p < 0.001 for all). Interobserver agreements for visual scoring (weighted kappa = 0.869), nCBFintratumoral_GM(ICC = 0.958) and nCBFperitumoral_GM(ICC = 0.947) were all excellent.
Conclusions:
ASL-PWI performs well in differentiating PCNSL from GBM in both qualitative and quantitative analyses.
Key points:
• ASL-PWI performs well (AUC > 0.8) in differentiating PCNSL from GBM. • The visual scoring template demonstrated good diagnostic performance, similar to quantitative analysis. • nCBFperitumoraldemonstrated better diagnostic performance than nCBFintratumoralor visual scoring.
Purpose:
Aim of this retrospective study was to compare diagnostic accuracy of proposed automatic normalization method to quantify the relative cerebral blood volume (rCBV) with existing contra-lateral region of interest (ROI) based CBV normalization method for glioma grading using T1-weighted dynamic contrast enhanced MRI (DCE-MRI).
Material and methods:
Sixty patients with histologically confirmed gliomas were included in this study retrospectively. CBV maps were generated using T1-weighted DCE-MRI and are normalized by contralateral ROI based method (rCBV_contra), unaffected white matter (rCBV_WM) and unaffected gray matter (rCBV_GM), the latter two of these were generated automatically. An expert radiologist with >10years of experience in DCE-MRI and a non-expert with one year experience were used independently to measure rCBVs. Cutoff values for glioma grading were decided from ROC analysis. Agreement of histology with rCBV_WM, rCBV_GM and rCBV_contra respectively was studied using Kappa statistics and intra-class correlation coefficient (ICC).
Result:
The diagnostic accuracy of glioma grading using the measured rCBV_contra by expert radiologist was found to be high (sensitivity=1.00, specificity=0.96, p<0.001) compared to the non-expert user (sensitivity=0.65, specificity=0.78, p<0.001). On the other hand, both the expert and non-expert user showed similar diagnostic accuracy for automatic rCBV_WM (sensitivity=0.89, specificity=0.87, p=0.001) and rCBV_GM (sensitivity=0.81, specificity=0.78, p=0.001) measures. Further, it was also observed that, contralateral based method by expert user showed highest agreement with histological grading of tumor (kappa=0.96, agreement 98.33%, p<0.001), however; automatic normalization method showed same percentage of agreement for both expert and non-expert user. rCBV_WM showed an agreement of 88.33% (kappa=0.76,p<0.001) with histopathological grading.
Conclusion:
It was inferred from this study that, in the absence of expert user, automated normalization of CBV using the proposed method could provide better diagnostic accuracy compared to the manual contralateral based approach.
Patients with brain tumor exhibit wide-ranging prognoses and functional implications of their disease and treatments. In general, the supportive care needs of patients with brain tumor, including disabling effects, have been recognized to be high. This review (1) briefly summarizes brain tumor types, treatments, and prognostic information for the rehabilitation clinician; (2) reviews evidence for rehabilitation, including acute inpatient rehabilitation and cognitive rehabilitation, and the approaches to selected common symptom and medical management issues; and (3) examines emerging data about survivorship, such as employment, community integration, and fitness.
Background
Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.
Methods
We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).
Results
We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).
Conclusion
MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.
Purpose
Dynamic contrast enhanced (DCE) MRI is used to grade and to monitor the progression of glioma while on treatment. Usually, a fixed hematocrit (Hct) value for adults is assumed to be ~45%; however, it is actually known for individual variations. Purpose of this study was to investigate the effect of measured Hct values in glioma grading using DCE-MRI.
Materials and methods
Fifty glioma patients were included in this study. Kinetic and hemodynamic parameters were estimated for each patient using assumed as well as measured Hct values. To look the changes in Hct value over time, Hct was measured multiple times from 10 of these glioma patients who were on treatment. Simulation was done to look for the effect of extreme variations of Hct values on perfusion metrics. The data was compared to look for significant differences in the perfusion metrics derived from assumed and measured Hct values.
Results
The measured Hct value in patients was found to be (40.4 ± 4.28)%. The sensitivity and specificity of DCE-MRI parameters in glioma grading were not significantly influenced by using measured vis-a-vis assumed Hct values. The serial Hct values from 10 patients who were on treatment showed a fluctuation of 15–20% over time. The simulated data showed linear influence of Hct values on kinetic parameters. The tumor grading was altered on altering the Hct values in borderline cases.
Conclusion
Hct values influence the hemodynamic and kinetic metrics linearly and may affect glioma grading. However, perfusion metrics values might change significantly with large change in Hct values, especially in patients who are on chemotherapy necessitating its use in the DCE model.
Purpose:
Dynamic contrast enhanced (DCE) MRI is used to grade and to monitor the progression of glioma while on treatment. Usually, a fixed Hematocrit (Hct) value for adults is assumed to be ~45%; however, it is actually known for individual variations. Purpose of this study was to investigate the effect of measured Hct values in glioma grading using DCE-MRI.
Materials and methods:
Fifty glioma patients were included in this study. Kinetic and hemodynamic parameters were estimated for each patient using assumed as well as measured Hct values. To look the changes in Hct value over time, Hct was measured multiple times from 10 of these glioma patients who were on treatment. Simulation was done to look for the effect of extreme variations of Hct values on perfusion metrics. The data was compared to look for significant differences in the perfusion metrics derived from assumed and measured Hct values.
Results:
The measured Hct value in patients was found to be (40.4±4.28)%. The sensitivity and specificity of DCE-MRI parameters in glioma grading were not significantly influenced by using measured vis-a-vis assumed Hct values. The serial Hct values from 10 patients who were on treatment showed a fluctuation of 15-20% over time. The simulated data showed linear influence of Hct values on kinetic parameters. The tumor grading was altered on altering the Hct values in borderline cases.
Conclusion:
Hct values influence the hemodynamic and kinetic metrics linearly and may affect glioma grading. However, perfusion metrics values might change significantly with large change in Hct values, especially in patients who are on chemotherapy necessitating its use in the DCE model.
Glioblastomas and malignant gliomas are the most common primary malignant brain tumors, with an annual incidence of 5.26 per 100,000 population or 17,000 new diagnoses per year. These tumors are typically associated with a dismal prognosis and poor quality of life.
To review the clinical management of malignant gliomas, including genetic and environmental risk factors such as cell phones, diagnostic pitfalls, symptom management, specific antitumor therapy, and common complications.
Search of PubMed references from January 2000 to May 2013 using the terms glioblastoma, glioma, malignant glioma, anaplastic astrocytoma, anaplastic oligodendroglioma, anaplastic oligoastrocytoma, and brain neoplasm. Articles were also identified through searches of the authors' own files. Evidence was graded using the American Heart Association classification system.
Only radiation exposure and certain genetic syndromes are well-defined risk factors for malignant glioma. The treatment of newly diagnosed glioblastoma is based on radiotherapy combined with temozolomide. This approach doubles the 2-year survival rate to 27%, but overall prognosis remains poor. Bevacizumab is an emerging treatment alternative that deserves further study. Grade III tumors have been less well studied, and clinical trials to establish standards of care are ongoing. Patients with malignant gliomas experience frequent clinical complications, including thromboembolic events, seizures, fluctuations in neurologic symptoms, and adverse effects from corticosteroids and chemotherapies that require proper management and prophylaxis.
Glioblastoma remains a difficult cancer to treat, although therapeutic options have been improving. Optimal management requires a multidisciplinary approach and knowledge of potential complications from both the disease and its treatment.
This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions.
Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images.
PurposeTo estimate precontrast tissue parameter (T10) using fast spin echo (FSE) and to quantify physiological and hemodynamic parameters with leakage correction using T1-weighted dynamic contrast-enhanced (DCE) perfusion imaging.Materials and Methods
Voxel-wise T10 computation was performed followed by the analysis of T1-weighted DCE perfusion data for the conversion of signal intensity time curve to concentration time curve, estimation of hemodynamic and physiological perfusion indices, and a method for leakage correction. Validations of accuracy of the computations have also been carried out.ResultsThe computed T10 and hemodynamic perfusion indices in normal white and gray matter were in good agreement with the literature values. Physiological perfusion indices in these regions were found negligible, validating computations. Cerebral blood volume (CBV) values change negligibly over the length of concentration time curve in white matter, gray matter, and lesion (CBVcorrected), while CBVuncorrected (lesion) shows linear increase over time.ConclusionT1-weighted DCE perfusion data along with FSE-based T1 estimation can be used for an accurate estimation of hemodynamic and physiological perfusion indices. J. Magn. Reson. Imaging 2007;26:871–880. © 2007 Wiley-Liss, Inc.
Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data.
The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis.
Many applied researchers analyzing longitudinal data share a common misconception: that specialized statistical software is necessary to fit hierarchical linear models (also known as linear mixed models [LMMs], or multilevel models) to longitudinal data sets. Although several specialized statistical software programs of high quality are available that allow researchers to fit these models to longitudinal data sets (e.g., HLM), rapid advances in general purpose statistical software packages have recently enabled analysts to fit these same models when using preferred packages that also enable other more common analyses. One of these general purpose statistical packages is SPSS, which includes a very flexible and powerful procedure for fitting LMMs to longitudinal data sets with continuous outcomes. This article aims to present readers with a practical discussion of how to analyze longitudinal data using the LMMs procedure in the SPSS statistical software package.
In recent years, the use and abuse of statistics in the medical literature has extensively been reviewed. Amongst others, the importance of the P-value has been challenged and the use of misleading graphics, including 3-dimensional displays, has been criticized. The ease of access to more complex statistical procedures, since the introduction of several statistical software packages for personal computers, has been identified as one of the factors involved in the misuse of statistics. Therefore, we have developed a new computer program that includes those statistical procedures commonly encountered in the medical literature and in statistical textbooks for medical researchers. More complex statistical analyses are not implemented in the software. If researchers with limited statistical training require more sophisticated statistical analyses, they should refer to a statistician, not to a more complete statistical software package.
The purpose of this study was to clarify the efficacy of single-voxel proton magnetic resonance spectroscopy (MRS) in differentiating high-grade glioma from metastasis. Thirty-one high-grade gliomas (11 anaplastic gliomas and 20 glioblastomas) and 25 metastases were studied. Proton MRS was performed using point-resolved spectroscopy with echo times (TEs) of both 136 and 30 ms. The peaks for lipid were evaluated at short TE, and those for N-acetyl-aspartate (NAA), creatine (Cr), and choline-containing compounds (Cho) were assessed at long TE. All the tumors exhibited a strong Cho peak at long TE. Twenty-one of 25 metastases showed no definite Cr peak. The remaining 4 metastases showed NAA and Cr peaks; however, the presence of NAA and relatively high NAA/Cr ratio (1.58+/-0.56) indicated normal brain contamination. All the gliomas, except for a single glioblastoma, showed a Cr peak with (n=16) or without (n=14) NAA. At short TE all metastases and glioblastomas showed definite lipid or lipid/lactate mixture, but anaplastic gliomas showed no definite lipid signal. Intratumoral Cr suggests glioma. Absence of Cr indicates metastasis. Definite lipid signal indicates cellular necrosis in glioblastoma and metastasis, and no lipid signal may exclude metastases.
Glioblastoma multiforme (GBM) and single brain metastasis (MET) are the 2 most common malignant brain tumors that can appear similar on anatomic imaging but require vastly different treatment strategy. The purpose of our study was to determine whether the peak height and the percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MR imaging could differentiate GBM and MET.
Forty-three patients with histopathologic diagnosis of GBM (n=27) or MET (n=16) underwent DSC perfusion MR imaging in addition to anatomic MR imaging before surgery. Regions of interest were drawn around the nonenhancing peritumoral T2 lesion (PTL) and the contrast-enhancing lesion (CEL). T2* signal intensity-time curves acquired during the first pass of gadolinium contrast material were converted to the changes in relaxation rate to yield T2* relaxivity (Delta R2*) curve. The peak height of maximal signal intensity drop and the percentage of signal intensity recovery at the end of first pass were measured for each voxel in the PTL and CEL regions of the tumor.
The average peak height for the PTL was significantly higher (P=.04) in GBM than in MET. The average percentage of signal intensity recovery was significantly reduced in PTL (78.4% versus 82.8%; P=.02) and in CEL (62.5% versus 80.9%, P<.01) regions of MET compared with those regions in the GBM group.
The findings of our study show that the peak height and the percentage of signal intensity recovery derived from the Delta R2* curve of DSC perfusion MR imaging can differentiate GBM and MET.
Computational radiomics system to decode the radiographic phenotype
- J J Van Griethuysen
- A Fedorov
- C Parmar
Van Griethuysen JJ, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77:e104-e107. doi:10.1158/0008-5472.CAN-17-0339
Radiogenomic analysis of hypoxia pathway is predictive of overall survival in glioblastoma
- N Beig
- J Patel
- P Prasanna
Beig N, Patel J, Prasanna P, et al. Radiogenomic analysis of hypoxia pathway is predictive of overall survival in glioblastoma. Sci Rep. 2018;8:1-11. doi:
10.1038/s41598-017-18310-0
Radiogenomics to characterize regional genetic heterogeneity in glioblastoma
- L S Hu
- S Ning
- J M Eschbacher
Hu LS, Ning S, Eschbacher JM, et al. Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol. 2017;19:128-137.
doi:10.1093/neuonc/now135
Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI. BMC Cancer
- J Zhou
- J Lu
- C Gao
Zhou J, Lu J, Gao C, et al. Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI. BMC Cancer. 2020;20:1-10. doi:10.1186/s12885-020-6523-2
Quantification of Radiomics features of Peritumoral Vasogenic Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic contrast-enhanced Perfusion analysis
- P S Parvaze
- R Bhattacharjee
- Y K Verma
SUPPORTING INFORMATION
Additional supporting information can be found online in the Supporting Information section at the end of this article.
How to cite this article: Parvaze PS, Bhattacharjee R, Verma YK, et al. Quantification of Radiomics features of Peritumoral Vasogenic
Edema extracted from fluid-attenuated inversion recovery images in glioblastoma and isolated brain metastasis, using T1-dynamic
contrast-enhanced Perfusion analysis. NMR in Biomedicine. 2022;e4884. doi:10.1002/nbm.4884