Neuroradiological Viewpoint on the Diagnostics of Space-Occupying Brain Lesions

Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images

OBJECTIVE

The primary aim of this research was to harness the capabilities of deep learning to enhance neurosurgical procedures, focusing on accurate tumor boundary delineation and classification. Through advanced diagnostic tools, we aimed to offer surgeons a more insightful perspective during surgeries, improving surgical outcomes and patient care.

METHODS

The study deployed the Mask R-convolutional neural network (CNN) architecture, leveraging its sophisticated features to process and analyze data from surgical microscope videos and preoperative magnetic resonance images. Resnet101 and Resnet50 backbone networks are used in the Mask R-CNN method, and experimental results are given. We subsequently tested its performance across various metrics, such as accuracy, precision, recall, dice coefficient (DICE), and Jaccard index. Deep learning models were trained from magnetic resonance imaging and surgical microscope images, and the classification result obtained for each patient was combined with the weighted average.

RESULTS

The algorithm exhibited remarkable capabilities in distinguishing among meningiomas, metastases, and high-grade glial tumors. Specifically, for the Mask R-CNN Resnet 101 architecture, precision, recall, DICE, and Jaccard index values were recorded as 96%, 93%, 91%, and 84%, respectively. Conversely, for the Mask R-CNN Resnet 50 architecture, these values stood at 94%, 89%, 89%, and 82%. Additionally, the model achieved an impressive DICE score range of 94%-95% and an accuracy of 98% in pathology estimation.

CONCLUSIONS

As illustrated in our study, the confluence of deep learning with neurosurgical procedures marks a transformative phase in medical science. The results are promising but underscore diverse data sets' significance for training and refining these deep learning models.

Metastatic pituitary tumors: an institutional case series

PURPOSE

Pituitary carcinomas are a rare entity that respond poorly to multimodal therapy. Patients follow a variable disease course that remains ill-defined.

METHODS

We present an institutional case series of patients treated for pituitary carcinomas over a 30-year period from 1992 to 2022. A systematic review was conducted to identify prior case series of patients with pituitary carcinomas.

RESULTS

Fourteen patients with a mean age at pituitary carcinoma diagnosis of 52.5 years (standard deviation [SD] 19.4) met inclusion criteria. All 14 patients had tumor subtypes confirmed by immunohistochemistry and hormone testing, with the most common being ACTH-producing pituitary adenomas (n = 12). Patients had a median progression-free survival (PFS) of 1.4 years (range 0.7-10.0) and a median overall survival (OS) of 8.4 years (range 2.3-24.0) from pituitary adenoma diagnosis. Median PFS and OS were 0.6 years (range 0.0-2.2) and 1.5 years (range 0.1-9.6) respectively upon development of metastases. Most patients (n = 12) had locally invasive disease to the cavernous sinus, dorsum sellae dura, or sphenoid sinus prior to metastasis. Common sites of metastasis included the central nervous system, liver, lung, and bone. In a pooled analysis including additional cases from the literature, treatment of metastases with chemotherapy or a combination of radiation therapy and chemotherapy significantly prolonged PFS (p = 0.02), while failing to significantly improve OS (p = 0.14).

CONCLUSION

Pituitary carcinomas are highly recurrent, heterogenous tumors with variable responses to treatment. Multidisciplinary management with an experienced neuro-endocrine and neuro-oncology team is needed given the unrelenting nature of this disease.

The value of convolutional neural networks-based deep learning model in differential diagnosis of space-occupying brain diseases

Objectives

It is still a challenge to differentiate space-occupying brain lesions such as tumefactive demyelinating lesions (TDLs), tumefactive primary angiitis of the central nervous system (TPACNS), primary central nervous system lymphoma (PCNSL), and brain gliomas. Convolutional neural networks (CNNs) have been used to analyze complex medical data and have proven transformative for image-based applications. It can quickly acquire diseases' radiographic features and correct doctors' diagnostic bias to improve diagnostic efficiency and accuracy. The study aimed to assess the value of CNN-based deep learning model in the differential diagnosis of space-occupying brain diseases on MRI.

Methods

We retrospectively analyzed clinical and MRI data from 480 patients with TDLs (n = 116), TPACNS (n = 64), PCNSL (n = 150), and brain gliomas (n = 150). The patients were randomly assigned to training (n = 240), testing (n = 73), calibration (n = 96), and validation (n = 71) groups. And a CNN-implemented deep learning model guided by clinical experts was developed to identify the contrast-enhanced T1-weighted sequence lesions of these four diseases. We utilized accuracy, sensitivity, specificity, and area under the curve (AUC) to evaluate the performance of the CNN model. The model's performance was then compared to the neuroradiologists' diagnosis.

Results

The CNN model had a total accuracy of 87% which was higher than senior neuroradiologists (74%), and the AUC of TDLs, PCNSL, TPACNS and gliomas were 0.92, 0.92, 0.89 and 0.88, respectively.

Conclusion

The CNN model can accurately identify specific radiographic features of TDLs, TPACNS, PCNSL, and gliomas. It has the potential to be an effective auxiliary diagnostic tool in the clinic, assisting inexperienced clinicians in reducing diagnostic bias and improving diagnostic efficiency.

MR imaging profile and histopathological characteristics of tumour vasculature, cell density and proliferation rate define two distinct growth patterns of human brain metastases from lung cancer

PURPOSE

Non-invasive prediction of the tumour of origin giving rise to brain metastases (BMs) using MRI measurements obtained in radiological routine and elucidating the biological basis by matched histopathological analysis.

METHODS

Preoperative MRI and histological parameters of 95 BM patients (female, 50; mean age 59.6 ± 11.5 years) suffering from different primary tumours were retrospectively analysed. MR features were assessed by region of interest (ROI) measurements of signal intensities on unenhanced T1-, T2-, diffusion-weighted imaging and apparent diffusion coefficient (ADC) normalised to an internal reference ROI. Furthermore, we assessed BM size and oedema as well as cell density, proliferation rate, microvessel density and vessel area as histopathological parameters.

RESULTS

Applying recursive partitioning conditional inference trees, only histopathological parameters could stratify the primary tumour entities. We identified two distinct BM growth patterns depending on their proliferative status: Ki67_high BMs were larger (p = 0.02), showed less peritumoural oedema (p = 0.02) and showed a trend towards higher cell density (p = 0.05). Furthermore, Ki67_high BMs were associated with higher DWI signals (p = 0.03) and reduced ADC values (p = 0.004). Vessel density was strongly reduced in Ki67_high BM (p < 0.001). These features differentiated between lung cancer BM entities (p ≤ 0.03 for all features) with SCLCs representing predominantly the Ki67_high group, while NSCLCs rather matching with Ki67_low features.

CONCLUSION

Interpretable and easy to obtain MRI features may not be sufficient to predict directly the primary tumour entity of BM but seem to have the potential to aid differentiating high- and low-proliferative BMs, such as SCLC and NSCLC.

Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today's clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism ("oxygen metabolic radiomics") and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO₂) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO₂; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO₂ and mitoPO₂ values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis.

Merkel cell carcinoma brain metastasis with radiological findings mimicking primary CNS lymphoma: illustrative case

BACKGROUND

Merkel cell carcinoma (MCC) is a rare and aggressive neuroendocrine tumor with a high likelihood of distant metastasis. Approximately 30 cases of MCC brain metastasis have been reported. The authors report a case of MCC brain metastasis with imaging findings mimicking primary central nervous system lymphoma.

OBSERVATIONS

A 69-year-old asymptomatic White female with a past medical history of rheumatoid arthritis and MCC of the right cheek with no known regional or distant spread presented with a right frontal lobe lesion discovered incidentally on a surveillance scan. Brain magnetic resonance imaging revealed a vividly enhancing homogeneous lesion with restricted diffusion on diffusion-weighted imaging and corresponding apparent diffusion coefficient maps. Imaging characteristics suggested a highly cellular mass consistent with primary central nervous system lymphoma; however, given the likelihood of metastasis, resection was recommended. An intraoperative frozen section suggested lymphoma. However, further examination revealed positive cytokeratin 20 staining for a tumor, and a final diagnosis of MCC brain metastasis was made.

LESSONS

Imaging characteristics of MCC brain metastasis can vary widely. A high level of suspicion should be maintained in a patient with a known history of MCC. Aggressive resection is recommended, regardless of appearance on scans or pathology of frozen sections, because MCC can mimic other intracranial pathologies.

Pituitary carcinoma - case series and review of the literature

Although pituitary adenomas (PAs) account for 15% of intracranial tumors, pituitary carcinomas (PCs) are a rare entity. Most commonly, PCs evolve from aggressive PAs invading the surrounding structures and eventually leading to metastatic lesions. Due to the low incidence, the diagnosis and treatment remains challenging. We report a case series of five patients with pituitary carcinoma (PC) treated in our center. At first diagnosis 3 patients had an ACTH-producing adenoma, 1 a prolactinoma and 1 a double secreting adenoma (GH and prolactin). The mean time interval from initial diagnosis to diagnosis of PC was 10.7 years (range 5-20 years). All patients underwent multiple surgical resections and radiotherapy. Four patients were treated with temozolomide for metastatic disease. One patient with concomitant radiochemotherapy for local recurrence. Temozolomide led to a stable disease in 2 patients. One patient had a progressive disease after 9 cycles of temozolomide. In absence of standard treatment, immunotherapy was initiated, resulting in a stable disease. We report five cases of PCs. Three patients obtained a stable disease after tailored multidisciplinary treatment. Additionally, one patient was treated with immunotherapy, opening a new treatment option in PCs. Overall, PCs are rare intracranial neoplasms occurring several years after the initial diagnosis of aggressive PAs. Currently, the absence of predictive factors for an aggressive clinical course, provokes a challenging management.

Case report: Cryptogenic giant brain abscess caused by Providencia rettgeri mimicking stroke and tumor in a patient with impaired immunity

The highly lethal cryptogenic brain abscess can be easily misdiagnosed. However, cryptogenic brain abscess caused by Providencia rettgeri is rarely reported. We present the case of a cryptogenic Providencia rettgeri brain abscess and analyze the clinical manifestations, imaging findings, treatment, and outcome to improve the level of awareness, aid in accurate diagnosis, and highlight effective clinical management. A 39-year-old man was admitted to the hospital after experiencing acute speech and consciousness disorder for 1 day. The patient had a medical history of nephrotic syndrome and membranous nephropathy requiring immunosuppressant therapy. Magnetic resonance imaging revealed giant, space-occupying lesions involving the brain stem, basal ganglia, and temporal-parietal lobes without typical ring enhancement, mimicking a tumor. Initial antibiotic treatment was ineffective. Afterward, pathogen detection in cerebrospinal fluid using metagenomic next-generation sequencing revealed Providencia rettgeri. Intravenous maximum-dose ampicillin was administered for 5 weeks, and the patient's symptoms resolved. Cryptogenic Providencia rettgeri brain abscess typically occurs in patients with impaired immunity. Our patient exhibited a sudden onset with non-typical neuroimaging findings, requiring differentiation of the lesion from stroke and brain tumor. Metagenomic next-generation sequencing was important in identifying the pathogen. Rapid diagnosis and appropriate use of antibiotics were key to obtaining a favorable outcome.

Physiological MRI Biomarkers in the Differentiation Between Glioblastomas and Solitary Brain Metastases

PURPOSE

Glioblastomas (GB) and solitary brain metastases (BM) are the most common brain tumors in adults. GB and BM may appear similar in conventional magnetic resonance imaging (cMRI). Their management strategies, however, are quite different with significant consequences on clinical outcome. The aim of this study was to evaluate the usefulness of a previously presented physiological MRI approach scoping to obtain quantitative information about microvascular architecture and perfusion, neovascularization activity, and oxygen metabolism to differentiate GB from BM.

PROCEDURES

Thirty-three consecutive patients with newly diagnosed, untreated, and histopathologically confirmed GB or BM were preoperatively examined with our physiological MRI approach as part of the cMRI protocol.

RESULTS

Physiological MRI biomarker maps revealed several significant differences in the pathophysiology of GB and BM: Central necrosis was more hypoxic in GB than in BM (30 %; P = 0.036), which was associated with higher neovascularization activity (65 %; P = 0.043) and metabolic rate of oxygen (48 %; P = 0.004) in the adjacent contrast-enhancing viable tumor parts of GB. In peritumoral edema, GB infiltration caused neovascularization activity (93 %; P = 0.018) and higher microvascular perfusion (30 %; P = 0.022) associated with higher tissue oxygen tension (33 %; P = 0.020) and lower oxygen extraction from vasculature (32 %; P = 0.040).

CONCLUSION

Our physiological MRI approach, which requires only 7 min of extra data acquisition time, might be helpful to noninvasively distinguish GB and BM based on pathophysiological differences. However, further studies including more patients are required.

Interventions to reduce the time to diagnosis of brain tumours

BACKGROUND

Brain tumours are recognised as one of the most difficult cancers to diagnose because presenting symptoms, such as headache, cognitive symptoms, and seizures, may be more commonly attributable to other, more benign conditions. Interventions to reduce the time to diagnosis of brain tumours include national awareness initiatives, expedited pathways, and protocols to diagnose brain tumours, based on a person's presenting symptoms and signs; and interventions to reduce waiting times for brain imaging pathways. If such interventions reduce the time to diagnosis, it may make it less likely that people experience clinical deterioration, and different treatment options may be available.

OBJECTIVES

To systematically evaluate evidence on the effectiveness of interventions that may influence: symptomatic participants to present early (shortening the patient interval), thresholds for primary care referral (shortening the primary care interval), and time to imaging diagnosis (shortening the secondary care interval and diagnostic interval). To produce a brief economic commentary, summarising the economic evaluations relevant to these interventions.

SEARCH METHODS

For evidence on effectiveness, we searched CENTRAL, MEDLINE, and Embase from January 2000 to January 2020; Clinicaltrials.gov to May 2020, and conference proceedings from 2014 to 2018. For economic evidence, we searched the UK National Health Services Economic Evaluation Database from 2000 to December 2014.

SELECTION CRITERIA

We planned to include studies evaluating any active intervention that may influence the diagnostic pathway, e.g. clinical guidelines, direct access imaging, public health campaigns, educational initiatives, and other interventions that might lead to early identification of primary brain tumours. We planned to include randomised and non-randomised comparative studies. Included studies would include people of any age, with a presentation that might suggest a brain tumour.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed titles identified by the search strategy, and the full texts of potentially eligible studies. We resolved discrepancies through discussion or, if required, by consulting another review author.

MAIN RESULTS

We did not identify any studies for inclusion in this review. We excluded 115 studies. The main reason for exclusion of potentially eligible intervention studies was their study design, due to a lack of control groups. We found no economic evidence to inform a brief economic commentary on this topic.

AUTHORS' CONCLUSIONS

In this version of the review, we did not identify any studies that met the review inclusion criteria for either effectiveness or cost-effectiveness. Therefore, there is no evidence from good quality studies on the best strategies to reduce the time to diagnosis of brain tumours, despite the prioritisation of research on early diagnosis by the James Lind Alliance in 2015. This review highlights the need for research in this area.

Added Value of Spectroscopy to Perfusion MRI in the Differential Diagnostic Performance of Common Malignant Brain Tumors

BACKGROUND AND PURPOSE

Perfusion and spectroscopic MR imaging provide noninvasive physiologic and metabolic characterization of tissues, which can help in differentiating brain tumors. We investigated the diagnostic role of perfusion and spectroscopic MR imaging using individual and combined classifiers of these modalities and assessed the added performance value that spectroscopy can provide to perfusion using optimal combined classifiers that have the highest differential diagnostic performance to discriminate lymphomas, glioblastomas, and metastases.

MATERIALS AND METHODS

From January 2013 to January 2016, fifty-five consecutive patients with histopathologically proved lymphomas, glioblastomas, and metastases were included after undergoing MR imaging. The perfusion parameters (maximum relative CBV, maximum percentage of signal intensity recovery) and spectroscopic concentration ratios (lactate/Cr, Cho/NAA, Cho/Cr, and lipids/Cr) were analyzed individually and in optimal combinations. Differences among tumor groups, differential diagnostic performance, and differences in discriminatory performance of models with quantification of the added performance value of spectroscopy to perfusion were tested using 1-way ANOVA models, receiver operating characteristic analysis, and comparisons between receiver operating characteristic analysis curves using a bivariate χ², respectively.

RESULTS

The highest differential diagnostic performance was obtained with the following combined classifiers: maximum percentage of signal intensity recovery-Cho/NAA to discriminate lymphomas from glioblastomas and metastases, significantly increasing the sensitivity from 82.1% to 95.7%; relative CBV-Cho/NAA to discriminate glioblastomas from lymphomas and metastases, significantly increasing the specificity from 92.7% to 100%; and maximum percentage of signal intensity recovery-lactate/Cr and maximum percentage of signal intensity recovery-Cho/Cr to discriminate metastases from lymphomas and glioblastomas, significantly increasing the specificity from 83.3% to 97.0% and 100%, respectively.

CONCLUSIONS

Spectroscopy yielded an added performance value to perfusion using optimal combined classifiers of these modalities, significantly increasing the differential diagnostic performances for these common brain tumors.

The value of whole lesion ADC histogram profiling to differentiate between morphologically indistinguishable ring enhancing lesions-comparison of glioblastomas and brain abscesses

Background

Morphologically similar appearing ring enhancing lesions in the brain parenchyma can be caused by a number of distinct pathologies, however, they consistently represent life-threatening conditions. The two most frequently encountered diseases manifesting as such are glioblastoma multiforme (GBM) and brain abscess (BA), each requiring disparate therapeutical approaches. As a result of their morphological resemblance, essential treatment might be significantly delayed or even ommited, in case results of conventional imaging remain inconclusive. Therefore, our study aimed to investigate, whether ADC histogram profiling reliably can distinguish between both entities, thus enhancing the differential diagnostic process and preventing treatment failure in this highly critical context.

Methods

103 patients (51 BA, 52 GBM) with histopathologically confirmed diagnosis were enrolled. Pretreatment diffusion weighted imaging (DWI) was obtained in a 1.5T system using b values of 0, 500, and 1000 s/mm². Whole lesion ADC volumes were analyzed using a histogram-based approach. Statistical analysis was performed using SPSS version 23.

Results

All investigated parameters were statistically different in comparison of both groups. Most importantly, ADCp10 was able to differentiate reliably between BA and GBM with excellent accuracy (0.948) using a cutpoint value of 70 × 10⁻⁵ mm² × s⁻¹.

Conclusions

ADC whole lesion histogram profiling provides a valuable tool to differentiate between morphologically indistinguishable mass lesions. Among the investigated parameters, the 10th percentile of the ADC volume distinguished best between GBM and BA.

Glioma grading by dynamic susceptibility contrast perfusion and 11C-methionine positron emission tomography using different regions of interest

PURPOSE

The use of dynamic susceptibility contrast (DSC) perfusion and ¹¹C-methionine positron emission tomography (MET-PET) for glioma grading is currently not standardized. The purpose of this study was to identify regions of interest (ROIs) that enable the best performance and clinical applicability in both methods, as well as to evaluate the complementarity of DSC perfusion and MET-PET in spatial hotspot definition.

METHODS

In 41 patient PET/MRI datasets, different ROIs were drawn: in T2-hyperintense tumour, in T2-hyperintense tumour and adjacent oedema and in tumour areas with contrast enhancement, altered perfusion or pathological radiotracer uptake. The performance of DSC perfusion and MET-PET using the different ROIs to distinguish high- and low-grade gliomas was assessed. The spatial overlap of hotspots identified by DSC perfusion and MET-PET was assessed visually.

RESULTS

ROIs in T2 fluid attenuated inversion recovery (FLAIR) sequence-hyperintense tumour revealed the most significant differences between high- and low-grade gliomas and reached the highest diagnostic performance in both DSC perfusion (p = 0.046; area under the curve = 0.74) and MET-PET (p = 0.007; area under the curve = 0.80). The combination of methods yielded an area under the curve of 0.80. Hotspots were completely overlapped in one half of the patients, partially overlapped in one third of the patients and present in only one method in approximately 20% of the patients.

CONCLUSIONS

For multi-parametric examinations with DSC perfusion and MET-PET, we recommend an ROI definition based on T2-hyperintense tumour. DSC perfusion and MET-PET contain complementary information concerning the spatial hotspot definition.

Glioblastoma multiforme presenting with an open ring pattern of enhancement on MR imaging

Background:

Intracerebral ring enhancing lesions can be the presentation of a variety of pathologies, including neoplasia, inflammation, and autoimmune demyelination. Use of a precise diagnostic algorithm is imperative in correctly treating these lesions and minimizing potential adverse treatment effects.

Case Description:

A 55-year-old patient presented to the hospital with complaints of a post-concussive syndrome and a non-focal neurologic exam. Imaging revealed a lesion with an open ring enhancement pattern, minimal surrounding vasogenic edema, and minimal mass effect. Given the minimal mass effect, small size of the lesion, and nonfocal neurological exam, we elected to pursue a comprehensive noninvasive neurologic workup because our differential ranged from inflammatory/infectious to neoplasm. Over the next 8 weeks, the patient's condition worsened, and repeat imaging showed marked enlargement of the lesion with a now closed ring pattern of enhancement with satellite lesions and a magnetic resonance (MR) spectroscopy and perfusion signature suggestive of neoplasm. The patient was taken to surgery for biopsy and debulking of the lesion. Surgical neuropathology examination revealed glioblastoma multiforme.

Conclusion:

The unique open ring enhancement pattern of this lesion on initial imaging is highly specific for a demyelinating process, however, high-grade glial neoplasms can also present with complex and irregular ring enhancement including an open ring sign. Therefore, other imaging modalities should be used, and close follow-up is warranted when the open ring sign is encountered.

Advanced neuroimaging in the clinic: critical appraisal of the evidence base

The shortage of high-quality systematic reviews in the field of radiology limits evidence-based integration of imaging methods into clinical practice and may perpetuate misconceptions regarding the efficacy and appropriateness of imaging techniques for specific applications. Diffusion tensor imaging for patients with mild traumatic brain injury (DTI-mTBI) and dynamic susceptibility contrast MRI for patients with glioma (DSC-glioma) are applications of quantitative neuroimaging, which similarly detect manifestations of disease where conventional neuroimaging techniques cannot. We performed a critical appraisal of reviews, based on the current evidence-based medicine methodology, addressing the ability of DTI-mTBI and DSC-glioma to (a) detect brain abnormalities and/or (b) predict clinical outcomes. 23 reviews of DTI-mTBI and 26 reviews of DSC-glioma met criteria for inclusion. All reviews addressed detection of brain abnormalities, whereas 12 DTI-mTBI reviews and 22 DSC-glioma reviews addressed prediction of a clinical outcome. All reviews were assessed using a critical appraisal worksheet consisting of 19 yes/no questions. Reviews were graded according to the total number of positive responses and the 2011 Oxford Centre for evidence-based medicine levels of evidence criteria. Reviews addressing DTI-mTBI detection had moderate quality, while those addressing DSC-glioma were of low quality. Reviews addressing prediction of outcomes for both applications were of low quality. Five DTI-mTBI reviews, but only one review of DSC-glioma met criteria for classification as a meta-analysis/systematic/quantitative review.

"Comet tail sign": A pitfall of post-gadolinium magnetic resonance imaging findings for metastatic brain tumors

A highly enhanced cap attached to the surface of metastatic tumors in the brain parenchyma is occasionally encountered on magnetic resonance (MR) images. This atypical enhanced cap tends to occur in severe peritumoral edema and may produce the characteristic bulge of a metastatic mass lesion termed the "comet tail sign" (CTS). The purpose of this study was to demonstrate the features of the CTS using MR imaging and pathological findings, and to clarify its clinical relevance. We selected 21 consecutive cases of newly diagnosed metastases from MR imaging studies that demonstrated the CTS; all had diffuse peritumoral edema. The MR T2-weighted images showed similarly homogenous and high intensity signals in both the tail and peritumoral edema. Fourteen of the 21 patients underwent surgical resection of their tumors, and 12 tails were separately removed for pathological examination, no tumor cells which revealed. We speculate that the CTS does not contain neoplastic tissues but is observed as a result of the leakage of contrast medium from the tumor body into the interstitial space of the white matter. Although CTS is a peculiar and uncommon enhancement pattern, it has clinical significance in determining the extent of the margin for invasive local treatments, such as surgical resection or stereotactic radiotherapy; this is particularly true in and near the eloquent areas.

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Advances in Magnetic Resonance Imaging and Positron Emission Tomography Imaging for Grading and Molecular Characterization of Glioma

In recent years, the management of glioma has evolved significantly, reflecting our better understanding of the underlying mechanisms of tumor development, tumor progression, and treatment response. Glioma grade, along with a number of underlying molecular and genetic biomarkers, has been recognized as an important prognostic and predictive factor that can help guide the management of patients. This article highlights advances in magnetic resonance imaging (MRI), including diffusion-weighted imaging, diffusion tensor imaging, magnetic resonance spectroscopy, dynamic contrast-enhanced imaging, and perfusion MRI, as well as position emission tomography using various tracers including methyl-(11)C-l-methionine and O-(2-(18)F-fluoroethyl)-l-tyrosine. Use of multiparametric imaging data has improved the diagnostic strength of imaging, introduced the potential to noninvasively interrogate underlying molecular features of low-grade glioma and to guide local therapies such as surgery and radiotherapy.

Bacterial brain abscess

Significant advances in the diagnosis and management of bacterial brain abscess over the past several decades have improved the expected outcome of a disease once regarded as invariably fatal. Despite this, intraparenchymal abscess continues to present a serious and potentially life-threatening condition. Brain abscess may result from traumatic brain injury, prior neurosurgical procedure, contiguous spread from a local source, or hematogenous spread of a systemic infection. In a significant proportion of cases, an etiology cannot be identified. Clinical presentation is highly variable and routine laboratory testing lacks sensitivity. As such, a high degree of clinical suspicion is necessary for prompt diagnosis and intervention. Computed tomography and magnetic resonance imaging offer a timely and sensitive method of assessing for abscess. Appearance of abscess on routine imaging lacks specificity and will not spare biopsy in cases where the clinical context does not unequivocally indicate infectious etiology. Current work with advanced imaging modalities may yield more accurate methods of differentiation of mass lesions in the brain. Management of abscess demands a multimodal approach. Surgical intervention and medical therapy are necessary in most cases. Prognosis of brain abscess has improved significantly in the recent decades although close follow-up is required, given the potential for long-term sequelae and a risk of recurrence.

High Resolution Imaging of Viscoelastic Properties of Intracranial Tumours by Multi-Frequency Magnetic Resonance Elastography

PURPOSE

In recent years Magnetic Resonance Elastography (MRE) emerged into a clinically applicable imaging technique. It has been shown that MRE is capable of measuring global changes of the viscoelastic properties of cerebral tissue. The purpose of our study was to evaluate a spatially resolved three-dimensional multi-frequent MRE (3DMMRE) for assessment of the viscoelastic properties of intracranial tumours.

METHODS

A total of 27 patients (63 ± 13 years) were included. All examinations were performed on a 3.0 T scanner, using a modified phase-contrast echo planar imaging sequence. We used 7 vibration frequencies in the low acoustic range with a temporal resolution of 8 dynamics per wave cycle. Post-processing included multi-frequency dual elasto-visco (MDEV) inversion to generate high-resolution maps of the magnitude |G*| and the phase angle φ of the complex valued shear modulus.

RESULTS

The tumour entities included in this study were: glioblastoma (n = 11), anaplastic astrocytoma (n = 3), meningioma (n = 7), cerebral metastasis (n = 5) and intracerebral abscess formation (n = 1). Primary brain tumours and cerebral metastases were not distinguishable in terms of |G*| and φ. Glioblastoma presented the largest range of |G*| values and a trend was delineable that glioblastoma were slightly softer than WHO grade III tumours. In terms of φ, meningiomas were clearly distinguishable from all other entities.

CONCLUSIONS

In this pilot study, while analysing the viscoelastic constants of various intracranial tumour entities with an improved spatial resolution, it was possible to characterize intracranial tumours by their mechanical properties. We were able to clearly delineate meningiomas from intraaxial tumours, while for the latter group an overlap remains in viscoelastic terms.

Contrast-enhanced MR imaging in neuroimaging

MR imaging without and with gadolinium-based contrast agents (GBCAs) is an important imaging tool for defining normal anatomy and characteristics of lesions. GBCAs have been used in contrast-enhanced MR imaging in defining and characterizing lesions of the central nervous system for more than 20 years. The combination of unenhanced and GBCA-enhanced MR imaging is the clinical gold standard for the noninvasive detection and delineation of most intracranial and spinal lesions. MR imaging has a high predictive value that rules out neoplasm and most inflammatory and demyelinating processes of the central nervous system.