Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database

Object The aim of this study was to estimate the accuracy of routine magnetic resonance (MR) imaging studies in the classification of brain tumors in terms of both cell type and grade of malignancy. Methods The authors retrospectively assessed the correlation between neuroimaging classifications and histopathological diagnoses by using multicenter database records from 393 patients with brain tumors. An ontology was devised to establish diagnostic agreement. Each tumor category was compared with the corresponding histopathological diagnoses by dichotomization. Sensitivity, specificity, positive and negative predictive values (PPVs and NPVs, respectively), and the Wilson 95% confidence intervals (CI) for each were calculated. In routine reporting of MR imaging examinations, tumor types and grades were classified with a high specificity (85.2–100%); sensitivity varied, depending on the tumor type and grade, alone or in combination. The recognition of broad diagnostic categories (neuroepithelial or meningeal lesions) was highly sensitive, whereas when both detailed type and grade were considered, sensitivity diverged, being highest in low-grade meningioma (sensitivity 100%, 95% CI 96.2–100.0%) and lowest in high-grade meningioma (sensitivity 0.0%, 95% CI 0.0–65.8%) and low-grade oligodendroglioma (sensitivity 15%, 95% CI 5.2–36.0%). In neuroepithelial tumors, sensitivity was inversely related to the precision in reporting of grade and cellular origin; “glioma” was a frequent neuroimaging classification associated with higher sensitivity in the corresponding category. The PPVs varied among categories, in general being greater than their prevalence in this dataset. The NPV was high in all categories (69.8–100%). Conclusions The PPVs and NPVs provided in this study may be used as estimates of posttest probabilities of diagnostic accuracy using MR imaging. This study targets the need for noninvasively increasing sensitivity in categorizing most brain tumor types while retaining high specificity, especially in the differentiation of high- and low-grade glial tumor classes.

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Cerebrospinal Fluid MicroRNA Signatures as Diagnostic Biomarkers in Brain Tumors

Cancers ◽

10.3390/cancers11101546 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1546 ◽

Cited By ~ 8

Author(s):

Alena Kopkova ◽

Jiri Sana ◽

Tana Machackova ◽

Marek Vecera ◽

Lenka Radova ◽

...

Keyword(s):

Cerebrospinal Fluid ◽

Brain Tumor ◽

Brain Tumors ◽

Low Grade ◽

Tumor Type ◽

Tumor Patients ◽

Primary Tumors ◽

Mirna Profiling ◽

Tumor Types ◽

Cns Malignancies

Central nervous system (CNS) malignancies include primary tumors that originate within the CNS as well as secondary tumors that develop as a result of metastatic spread. Circulating microRNAs (miRNAs) were found in almost all human body fluids including cerebrospinal fluid (CSF), and they seem to be highly stable and resistant to even extreme conditions. The overall aim of our study was to identify specific CSF miRNA patterns that could differentiate among brain tumors. These new biomarkers could potentially aid borderline or uncertain imaging results onto diagnosis of CNS malignancies, avoiding most invasive procedures such as stereotactic biopsy or biopsy. In total, 175 brain tumor patients (glioblastomas, low-grade gliomas, meningiomas and brain metastases), and 40 non-tumor patients with hydrocephalus as controls were included in this prospective monocentric study. Firstly, we performed high-throughput miRNA profiling (Illumina small RNA sequencing) on a discovery cohort of 70 patients and 19 controls and identified specific miRNA signatures of all brain tumor types tested. Secondly, validation of 9 candidate miRNAs was carried out on an independent cohort of 105 brain tumor patients and 21 controls using qRT-PCR. Based on the successful results of validation and various combination patterns of only 5 miRNA levels (miR-30e, miR-140, let-7b, mR-10a and miR-21-3p) we proposed CSF-diagnostic scores for each tumor type which enabled to distinguish them from healthy donors and other tumor types tested. In addition to this primary diagnostic tool, we described the prognostic potential of the combination of miR-10b and miR-196b levels in CSF of glioblastoma patients. In conclusion, we performed the largest study so far focused on CSF miRNA profiling in patients with brain tumors, and we believe that this new class of biomarkers have a strong potential as a diagnostic and prognostic tool in these patients.

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Characterization of astrocytomas, meningiomas, and pituitary adenomas by phosphorus magnetic resonance spectroscopy

Journal of Neurosurgery ◽

10.3171/jns.1991.74.3.0447 ◽

1991 ◽

Vol 74 (3) ◽

pp. 447-453 ◽

Cited By ~ 34

Author(s):

Douglas L. Arnold ◽

Joseph F. Emrich ◽

Eric A. Shoubridge ◽

Jean-Guy Villemure ◽

William Feindel

Keyword(s):

Magnetic Resonance ◽

Pituitary Adenomas ◽

Resonance Spectroscopy ◽

Low Grade ◽

Normal Brain ◽

Tumor Type ◽

Content Type ◽

Tumor Types ◽

Fine Print

✓ Phosphorus magnetic resonance (MR) spectroscopy allows noninvasive measurement of phosphate-containing compounds and pH within brain cells. The authors obtained localized phosphorus MR spectra from 10 normal brains, four low-grade astrocytomas, six glioblastomas, four meningiomas, and three pituitary adenomas and found differences in the spectra of each tumor type. Compared to normal brain, the spectra from low-grade astrocytomas showed a significant reduction of the phosphodiester (PDE) peak. Glioblastomas were characterized by a significant reduction of the PDE peak, elevation of the phosphomonoester (PME) peak, and a relatively alkaline intracellular pH. The spectra from meningiomas and pituitary adenomas were markedly different from the glial tumors. Meningiomas showed significant reductions in phosphocreatine, PDE, and inorganic phosphate, as well as a relatively alkaline pH. Pituitary adenomas resembled meningiomas, but had a much higher PME peak. Although the number of tumors studied was small, there appears to be a characteristic spectrum associated with these different tumor types. The present findings can be useful in the preoperative identification of these tumors and in furthering understanding of their growth and metabolism in vivo.

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Proton magnetic resonance spectroscopy in the distinction of high-grade cerebral gliomas from single metastatic brain tumors

Acta Radiologica ◽

10.3109/02841850903482901 ◽

2010 ◽

Vol 51 (3) ◽

pp. 316-325 ◽

Cited By ~ 68

Author(s):

Andres Server ◽

Roger Josefsen ◽

Bettina Kulle ◽

Jan Mæhlen ◽

Till Schellhorn ◽

...

Keyword(s):

Magnetic Resonance ◽

Brain Tumors ◽

Mr Imaging ◽

Brain Metastases ◽

High Grade ◽

Peritumoral Edema ◽

Metastatic Brain Tumors ◽

Predictive Values ◽

Cutoff Value ◽

High Grade Gliomas

Background: Brain metastases and primary high-grade gliomas, including glioblastomas multiforme (GBM) and anaplastic astrocytomas (AA), may be indistinguishable by conventional magnetic resonance (MR) imaging. Identification of these tumors may have therapeutic consequences. Purpose: To assess the value of MR spectroscopy (MRS) using short and intermediate echo time (TE) in differentiating solitary brain metastases and high-grade gliomas on the basis of differences in metabolite ratios in the intratumoral and peritumoral region. Material and Methods: We performed MR imaging and MRS in 73 patients with histologically verified intraaxial brain tumors: 53 patients with high-grade gliomas (34 GBM and 19 AA) and 20 patients with metastatic brain tumors. The metabolite ratios of Cho/Cr, Cho/NAA, and NAA/Cr at intermediate TE and the presence of lipids at short TE were assessed from spectral maps in the tumoral core, peritumoral edema, and contralateral normal-appearing white matter. The differences in the metabolite ratios between high-grade gliomas/GBM/AA and metastases were analyzed statistically. Cutoff values of Cho/Cr, Cho/NAA, and NAA/Cr ratios in the peritumoral edema, as well as Cho/Cr and NAA/Cr ratios in the tumoral core for distinguishing high-grade gliomas/GBM/AA from metastases were determined by receiver operating characteristic (ROC) curve analysis. Results: Significant differences were noted in the peritumoral Cho/Cr, Cho/NAA, and NAA/ Cr ratios between high-grade gliomas/GBM/AA and metastases. ROC analysis demonstrated a cutoff value of 1.24 for peritumoral Cho/Cr ratio to provide sensitivity, specificity, positive (PPV), and negative predictive values (NPV) of 100%, 88.9%, 80.0%, and 100%, respectively, for discrimination between high-grade gliomas and metastases. By using a cutoff value of 1.11 for peritumoral Cho/NAA ratio, the sensitivity was 100%, the specificity was 91.1%, the PPV was 83.3%, and the NPV was 100%. Conclusion: The results of this study demonstrate that MRS can differentiate high-grade gliomas from metastases, especially with peritumoral measurements, supporting the hypothesis that MRS can detect infiltration of tumor cells in the peritumoral edema.

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Prognostic histopathologic features of canine glial tumors

Veterinary Pathology ◽

10.1177/03009858211025795 ◽

2021 ◽

pp. 030098582110257

Author(s):

Joshua L. Merickel ◽

G. Elizabeth Pluhar ◽

Aaron Rendahl ◽

M. Gerard O’Sullivan

Keyword(s):

Brain Tumor ◽

Median Survival ◽

Low Grade ◽

Astrocytic Tumors ◽

Translational Model ◽

Poor Survival ◽

Whole Brain ◽

Tumor Types ◽

Tumor Biopsies ◽

Prognostic Data

Gliomas are relatively common tumors in aged dogs (especially brachycephalic breeds), and the dog is proving to be useful as a translational model for humans with brain tumors. Hitherto, there is relatively little prognostic data for canine gliomas and none on outcome related to specific histological features. Histologic sections of tumor biopsies from 33 dogs with glioma treated with surgical resection and immunotherapy and 21 whole brains obtained postmortem were reviewed. Tumors were diagnosed as astrocytic, oligodendroglial, or undefined glioma using Comparative Brain Tumor Consortium criteria. Putative features of malignancy were evaluated, namely, mitotic counts, glomeruloid vascularization, and necrosis. For biopsies, dogs with astrocytic tumors lived longer than those with oligodendroglial or undefined tumor types (median survival 743, 205, and 144 days, respectively). Dogs with low-grade gliomas lived longer than those with high-grade gliomas (median survival 734 and 194 days, respectively). Based on analysis of tumor biopsies, low mitotic counts, absence of glomeruloid vascularization, and absence of necrosis correlated with increased survival (median 293, 223, and 220 days, respectively), whereas high mitotic counts, glomeruloid vascularization, and necrosis correlated with poor survival (median 190, 170, and 154 days, respectively). Mitotic count was the only histological feature in biopsy samples that significantly correlated with survival ( P < .05). Whole-brain analyses for those same histologic features had similar and more robust correlations, and were statistically significant for all features ( P < .05). The small size of biopsy samples may explain differences between biopsy and whole-brain tumor data. These findings will allow more accurate prognosis for gliomas.

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Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

Brain Sciences ◽

10.3390/brainsci11030352 ◽

2021 ◽

Vol 11 (3) ◽

pp. 352

Author(s):

Isselmou Abd El Kader ◽

Guizhi Xu ◽

Zhang Shuai ◽

Sani Saminu ◽

Imran Javaid ◽

...

Keyword(s):

Neural Network ◽

Magnetic Resonance Imaging ◽

Brain Tumor ◽

Magnetic Resonance ◽

Brain Tumors ◽

Medical Image ◽

Resonance Imaging ◽

Feature Maps ◽

Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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Early changes measured by magnetic resonance imaging in cerebral blood flow, blood volume, and blood-brain barrier permeability following dexamethasone treatment in patients with brain tumors

Journal of Neurosurgery ◽

10.3171/jns.1999.90.2.0300 ◽

1999 ◽

Vol 90 (2) ◽

pp. 300-305 ◽

Cited By ~ 127

Author(s):

Leif Østergaard ◽

Fred H. Hochberg ◽

James D. Rabinov ◽

A. Gregory Sorensen ◽

Michael Lev ◽

...

Keyword(s):

Blood Flow ◽

Cerebral Blood Flow ◽

Magnetic Resonance ◽

Brain Tumors ◽

Mr Imaging ◽

Blood Volume ◽

Cerebral Blood Volume ◽

Clinical Effects ◽

Content Type ◽

Fine Print

Object. In this study the authors assessed the early changes in brain tumor physiology associated with glucocorticoid administration. Glucocorticoids have a dramatic effect on symptoms in patients with brain tumors over a time scale ranging from minutes to a few hours. Previous studies have indicated that glucocorticoids may act either by decreasing cerebral blood volume (CBV) or blood-tumor barrier (BTB) permeability and thereby the degree of vasogenic edema.Methods. Using magnetic resonance (MR) imaging, the authors examined the acute changes in CBV, cerebral blood flow (CBF), and BTB permeability to gadolinium-diethylenetriamine pentaacetic acid after administration of dexamethasone in six patients with brain tumors. In patients with acute decreases in BTB permeability after dexamethasone administration, changes in the degree of edema were assessed using the apparent diffusion coefficient of water.Conclusions. Dexamethasone was found to cause a dramatic decrease in BTB permeability and regional CBV but no significant changes in CBF or the degree of edema. The authors found that MR imaging provides a powerful tool for investigating the pathophysiological changes associated with the clinical effects of glucocorticoids.

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IBRDM: An Intelligent Framework for Brain Tumor Classification Using Radiomics- and DWT-based Fusion of MRI Sequences

ACM Transactions on Internet Technology ◽

10.1145/3434775 ◽

2022 ◽

Vol 22 (1) ◽

pp. 1-30

Author(s):

Rahul Kumar ◽

Ankur Gupta ◽

Harkirat Singh Arora ◽

Balasubramanian Raman

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Performance Metrics ◽

Tumor Classification ◽

Training Dataset ◽

Tumor Segmentation ◽

Discrete Wavelet ◽

Low Grade ◽

Brain Tumor Segmentation ◽

Mri Sequences

Brain tumors are one of the critical malignant neurological cancers with the highest number of deaths and injuries worldwide. They are categorized into two major classes, high-grade glioma (HGG) and low-grade glioma (LGG), with HGG being more aggressive and malignant, whereas LGG tumors are less aggressive, but if left untreated, they get converted to HGG. Thus, the classification of brain tumors into the corresponding grade is a crucial task, especially for making decisions related to treatment. Motivated by the importance of such critical threats to humans, we propose a novel framework for brain tumor classification using discrete wavelet transform-based fusion of MRI sequences and Radiomics feature extraction. We utilized the Brain Tumor Segmentation 2018 challenge training dataset for the performance evaluation of our approach, and we extract features from three regions of interest derived using a combination of several tumor regions. We used wrapper method-based feature selection techniques for selecting a significant set of features and utilize various machine learning classifiers, Random Forest, Decision Tree, and Extra Randomized Tree for training the model. For proper validation of our approach, we adopt the five-fold cross-validation technique. We achieved state-of-the-art performance considering several performance metrics, 〈 Acc , Sens , Spec , F1-score , MCC , AUC 〉 ≡ 〈 98.60%, 99.05%, 97.33%, 99.05%, 96.42%, 98.19% 〉, where Acc , Sens , Spec , F1-score , MCC , and AUC represents the accuracy, sensitivity, specificity, F1-score, Matthews correlation coefficient, and area-under-the-curve, respectively. We believe our proposed approach will play a crucial role in the planning of clinical treatment and guidelines before surgery.

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BIOM-10. PRECLINICAL PLATFORM FOR THE IDENTIFICATION OF DEUTERIUM MAGNETIC RESONANCE SPECTROSCOPY-BASED BIOMARKERS OF BRAIN TUMOR METABOLISM

Neuro-Oncology ◽

10.1093/neuonc/noab196.041 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi12-vi12

Author(s):

Georgios Batsios ◽

Meryssa Tran ◽

Céline Taglang ◽

Anne Marie Gillespie ◽

Sabrina Ronen ◽

...

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Magnetic Resonance Spectroscopy ◽

Brain Tumors ◽

Treatment Response ◽

Lactate Production ◽

Cell Suspensions ◽

Response To Therapy ◽

Resonance Spectroscopy

Abstract Metabolic reprogramming is a fundamental hallmark of cancer, which can be exploited for non-invasive tumor imaging. Deuterium magnetic resonance spectroscopy (2H-MRS) recently emerged as a novel, translational method of interrogating flux from 2H-labeled substrates to metabolic products. However, to date, preclinical studies have been performed in vivo, an endeavor which suffers from low-throughput and potential wastage of animal life, especially when considering studies of treatment response. Developing in vitro assays for monitoring metabolism of 2H-labeled substrates will enhance throughput, lead to the rapid evaluation of new 2H-based probes and enable identification of treatment response biomarkers, thereby allowing the best 2H-based probes to be translated for further in vivo assessment. The goal of this study was to develop a preclinical cell-based platform for quantifying metabolism of 2H-labeled probes in brain tumor models. Since the Warburg effect, which is characterized by elevated glycolytic production of lactate, is a metabolic phenotype of cancer, including brain tumors, we examined metabolism of 2H-glucose or 2H-pyruvate in patient-derived glioblastoma (GBM6) and oligodendroglioma (BT88) cells and compared to normal human astrocytes (NHACONTROL). Following incubation in media containing [6,6’-2H]glucose or [U-2H]pyruvate, 2H-MR spectra obtained from live cell suspensions showed elevated 2H-lactate production in GBM6 and BT88 cells relative to NHACONTROL. Importantly, 2H-lactate production from [6,6’-2H]glucose or from [U-2H]pyruvate was reduced in GBM6 or BT88 cells subjected to irradiation and temozolomide, which is standard of care for glioma patients, pointing to the utility of this method for detecting response to therapy. Collectively, we have, for the first time, demonstrated the ability to quantify metabolism of 2H-MRS probes in live cell suspensions and validated the utility of our assay for differentiating tumor from normal cells and assessing response to therapy. Our studies will expedite the identification of novel 2H-MRS probes for imaging brain tumors and potentially other types of cancer.

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EPID-17. COMPARATIVE EPIDEMIOLOGY OF PRIMARY BRAIN TUMORS AMONG ADOLESCENTS AND YOUNG ADULTS (AYA)

Neuro-Oncology ◽

10.1093/neuonc/noab196.350 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi89-vi89

Author(s):

Nayan Lamba ◽

Bryan Iorgulescu

Keyword(s):

Brain Tumor ◽

Brain Tumors ◽

Cox Regression ◽

Adolescent And Young Adult ◽

Lower Grade ◽

Diffuse Glioma ◽

Tumor Type ◽

Primary Brain Tumors ◽

Diffuse Gliomas ◽

Hiv Aids

Abstract INTRODUCTION We utilized national registry data to evaluate the unique epidemiology of primary adolescent and young adult (AYA) brain tumors according to the WHO2016 classification. METHODS AYA patients (15≤age≤39) presenting between 2004-2017 with a brain tumor were identified by ICD-O-3 coding from the National Cancer Database (comprising >70% of newly-diagnosed cancers in the U.S.), and compared to pediatric and adult populations. Epidemiology and overall survival (estimated by Kaplan-Meier techniques and multivariable Cox regression) were assessed by WHO2016 tumor type. RESULTS 108,705 AYA brain tumor patients were identified (56.9% female), compared to 23,928 pediatric (46.8% female) and 748,272 adult (55.6% female) patients. Among the 69.4% of AYA brain tumors with pathological diagnosis, diffuse gliomas (31.4%), sellar tumors (19.2%), and meningiomas (15.3%) predominated in both sexes. Diffuse glioma (31.4%), sellar (19.2%), cranial nerve (7.3%), and mesenchymal non-meningothelial (4.1%) tumors represented a greater proportion of AYA brain tumors than in either pediatric or adult populations. A majority of all intracranial GCTs (59.2%) and neuronal & mixed neuronal-glial tumors (51.6%) presented during AYA. Although the prevalence of diffuse gliomas was similar between AYAs and adults, AYA gliomas were more likely to be grade 2-3 astrocytomas (38.9% vs 14.3%) and oligodendrogliomas (19.3% vs 4.3%) than in adults. GBMs represented 76.0% of adult diffuse gliomas vs. only 25.7% of AYA diffuse gliomas, but with a similar prevalence of MGMT promoter methylation (40.8% vs 38.4%). Notably, 50.7% of AYA PCNSLs were associated with HIV/AIDS, vs only 7.1% in adults (p< 0.001). CONCLUSIONS The distribution, epidemiology, and survival outcomes of primary brain tumors in the AYA population are distinct from their pediatric and adult counterparts. Notably, AYA infiltrative gliomas were more often of lower grade than adults and AYA PCNSL were far more likely to be associated with HIV/AIDS. Primary brain tumors in AYA patients require specialized management.

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Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

Journal of Cerebral Blood Flow & Metabolism ◽

10.1038/jcbfm.2011.17 ◽

2011 ◽

Vol 31 (7) ◽

pp. 1623-1636 ◽

Cited By ~ 20

Author(s):

Eugene Kim ◽

Jiangyang Zhang ◽

Karen Hong ◽

Nicole E Benoit ◽

Arvind P Pathak

Keyword(s):

Brain Tumor ◽

Magnetic Resonance ◽

Brain Tumors ◽

Three Dimensional ◽

Region Of Interest ◽

Tumor Model ◽

Magnetic Resonance Microscopy ◽

Vascular Phenotype ◽

Brain Tumor Model ◽

Novel Method

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

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