¹H-MRS of intracranial meningiomas: what it can add to known clinical and MRI predictors of the histopathological and biological characteristics of the tumor?

Development of Hybrid radiomic Machine learning models for preoperative prediction of meningioma grade on multiparametric MRI

PURPOSE

To develop and compare machine learning models for distinguishing low and high grade meningiomas on multiparametric MRI.

METHODS

Pre-operative T1-weighted(T1), contrast-enhanced T1-weighted(T1CE), T2-weighted, T2 FLAIR, and DWI/ADC MRI sequences of meningiomas performed between 2000 and 2020 were retrospectively collected from a single tertiary hospital dedicated neurosurgical department. Tumours were manually segmented and handcrafted radiomic features were extracted. Deep learning features were extracted using a fine-tuned foundation model. Various oversampling techniques, feature selection algorithms and classifiers were trialled to build Handcrafted radiomics only (HRO) and handcrafted with deep learning radiomics (HDLR) models. Bootstrap was used for internal validation of model performance and calculating confidence intervals of metrices. Discrimination, calibration, feature importance and clinical utility of models were assessed via ROC AUC, calibration curve, Shapley values and decision curve analysis, respectively.

RESULTS

The analysis included 97 low grade and 18 high grade meningiomas. HRO and HDLR models had comparable diagnostic performance, using Random Forest and XGBoost respectively. They achieved mean (95 %CI): ROC AUC 0.825[0.662,0.952] and 0.794[0.662,0.948], specificity 0.913[0.793,0.952] and 0.892[0.796,0.983], sensitivity 0.499[0.204,1] and 0.509[0.225,0.851], NPV 0.909[0.851,0.971] and 0.909[0.851,0.972], and PPV 0.529[0.238,0.924] and 0.465[0.263,0.846], respectively for HRO and HDLR models. HRO and HDLR models selected 11-12 features, with T1 and T1CE having consistent importance.

CONCLUSION

HRO and HDLR can effectively predict meningioma grades preoperatively. Challenges remain in achieving consistent sensitivity and PPV. Larger, multi-centre studies are warranted to confirm our findings, but it holds promise for improving personalized treatment strategies and patient outcomes in meningioma management. Code is available on Github https://github.com/stephano41/radiomics_ai.

Intracranial meningioma: A review of recent and emerging data on the utility of preoperative imaging for management

Meningiomas are the most common neoplasms of the central nervous system, accounting for approximately 40% of all brain tumors. Surgical resection represents the mainstay of management for symptomatic lesions. Preoperative planning is largely informed by neuroimaging, which allows for evaluation of anatomy, degree of parenchymal invasion, and extent of peritumoral edema. Recent advances in imaging technology have expanded the purview of neuroradiologists, who play an increasingly important role in meningioma diagnosis and management. Tumor vascularity can now be determined using arterial spin labeling and dynamic susceptibility contrast-enhanced sequences, allowing the neurosurgeon or neurointerventionalist to assess patient candidacy for preoperative embolization. Meningioma consistency can be inferred based on signal intensity; emerging machine learning technologies may soon allow radiologists to predict consistency long before the patient enters the operating room. Perfusion imaging coupled with magnetic resonance spectroscopy can be used to distinguish meningiomas from malignant meningioma mimics. In this comprehensive review, we describe key features of meningiomas that can be established through neuroimaging, including size, location, vascularity, consistency, and, in some cases, histologic grade. We also summarize the role of advanced imaging techniques, including magnetic resonance perfusion and spectroscopy, for the preoperative evaluation of meningiomas. In addition, we describe the potential impact of emerging technologies, such as artificial intelligence and machine learning, on meningioma diagnosis and management. A strong foundation of knowledge in the latest meningioma imaging techniques will allow the neuroradiologist to help optimize preoperative planning and improve patient outcomes.

Supratentorial Meningioma Consistency Prediction Utilizing Tumor to Cerebellar Peduncle Intensity on T1 and T2-Weighted and Fluid Attenuated Inversion Recovery Magnetic Resonance Imaging Sequences

OBJECTIVE

Predicting meningioma consistency with preoperative imaging is critical for surgery planning. Preoperative T1 and T2-weighted and fluid attenuated inversion recovery magnetic resonance imaging (MRI) findings of supratentorial meningioma tumors were studied and compared with intraoperative supratentorial meningioma tumor consistency based on the Cavitron ultrasound surgical aspirator (CUSA) and ZADA grading scales in this cohort to predict the tumor consistency before surgery.

METHODS

MRI from 78 consecutive patients who underwent supratentorial meningioma tumor resection between 2018 and 2021 were evaluated preoperatively. An intraoperative tumor consistency grade was applied to these lesions prospectively by the operating surgeon based on CUSA and ZADA grading scales. Tumor/cerebellar peduncle T2-weighted intensity, tumor/cerebellar peduncle T1-weighted intensity (TCT1I), and tumor/cerebellar peduncle fluid attenuated inversion recovery intensity (TCFI) ratios were calculated. Tumor consistency grades and MRI intensity ratios were correlated using one-way ANOVA.

RESULTS

Of the 78 patients, 52 (66.7%) were female and 26 (33.3%) were male. Tumor volume correlated with tumor consistency grades on both CUSA (P = 0.005) and ZADA (P = 0.024) grading scales. Also patients age correlated with tumor consistency according to ZADA grading scale (P = 0.024). TCT1I (P = 0.009) and TCFI (P < 0.005) ratios correlated significantly with tumor consistency grade according to CUSA. Similarly, TCT1I (P = 0.0032) and TCFI (P = 0.001) ratios was significantly associated with tumor consistency according to ZADA grading scales.

CONCLUSIONS

Our findings suggest that higher tumor/cerebellar peduncle T2-weighted intensity and TCFI ratios correlate with softer tumors, while higher TCT1I ratios reveal firmer tumors. These data can assist the surgeon predict the supratentorial meningioma consistency before surgery.

Meningioma Related Epilepsy- Pathophysiology, Pre/postoperative Seizures Predicators and Treatment

Meningiomas are the most common primary brain tumors accounting for about 30% of all brain tumors. The vast majority of meningiomas are slow-growing and of benign histopathology rendering them curable by surgery alone. Symptomatic lesions depend on the location with signs of mass effect or neurological deficits. Seizures are the presenting symptoms in approximately 30% of cases, which negatively affect quality of life, limit independence, impair cognitive functioning, as well as increase the risk for psychiatric comorbidities including depression. Although surgical resection may offer seizure freedom in 60-90% of meningiomas, seizures persist after surgical resection in approximately 12-19% of patients. Anti-seizure medications (ASMs) are employed in management, however, are limited by adverse neurocognitive side-effects and inefficacy in some patients. The potential predictors of pre- and post-operative seizures in meningioma patients have been identified in the literature. Understanding various factors associated with seizure likelihood in meningioma patients can help guide more effective seizure control and allow for better determination of risk before and after surgery.

Evaluation of Magnetic Resonance Imaging for Microsurgical Efficacy and Relapse of Rolandic Meningioma

In this study, magnetic resonance imaging (MRI) was used to evaluate the relapse features of patients with Rolandic meningioma after the microsurgery. 53 patients with Rolandic meningioma were selected as the research objects, and they were divided into the relapse group (n = 16) and nonrelapse group (n = 37) according to whether patients had a relapse during the follow-up period. Differences in quality of life, ¹H-MRS index, vascular density, and cell proliferation between the two groups were assessed as well as imaging differences between the two groups were analyzed using MRI. The results showed that the patients' quality-of-life scores in the two groups increased notably after the surgical treatment (P < 0.05). Compared with the nonrelapse group, the proportion of irregular boundary and uneven enhancement of focal tissue in the relapse group was signally increased (P < 0.05). Compared with the nonrelapse group, cell proliferation index, vascular density and imaging index, mean tumor diameter, mean transit time (MTT), time to peak (TTP), fractional anisotropy (FA), choline (Cho)/N-acetylaspartic acid (NAA), Cho/creatine (Cr), lactic acid (Lac)/Cr, and the maximum value of relative cerebral blood volume (rCBVmax) in the relapse group were obviously increased (P < 0.05). However, the apparent dispersion coefficient, NAA/Cr, and Lac/NAA values decreased greatly (P < 0.05). To sum up, the microsurgical treatment helped improve the quality of life of patients with Rolandic meningioma, and MR imaging could be used to determine the relapse of Rolandic meningioma after microsurgical treatment.

Usefulness of T2 Relaxation Time for Quantitative Prediction of Meningioma Consistency

BACKGROUND

Meningioma consistency is one of the most critical factors affecting the difficulty of surgery. Although many studies have attempted to predict meningioma consistency via magnetic resonance imaging findings, no definitive method has been established, because most have been based on qualitative evaluations. Therefore, the present study examined the potential of the T2 relaxation time (T2 value), a tissue-specific quantitative parameter, for assessment of meningioma consistency.

METHODS

Eighteen surgically treated meningiomas in 16 patients were included in the present study. Preoperatively, the T2 values of all meningiomas were calculated pixel by pixel, and a T2 value distribution map was generated. A total of 27 tumor specimens (multiple specimens were procured if heterogeneous) were taken from these meningiomas, with each localization identified intraoperatively using image guidance. The consistency of the specimens was measured with a durometer, originally a device for measuring the hardness of material such as elastic rubber, and their water content was subsequently measured using wet and dry measurements.

RESULTS

A significant correlation was found between the T2 values of the matched locations identified by image guidance intraoperatively and the consistency measured using the durometer (r = -0.722; P < 0.01) and the water content (r = 0.621; P = 0.01). In addition, the water content correlated significantly with the durometer consistency (r = -0.677; P < 0.01).

CONCLUSIONS

The T2 values could be a reliable quantitative predictor of meningioma consistency, and the T2 value distribution map, which elucidated the internal structure of the tumor in detail, could provide helpful information for surgical resection.

Utility of 3T single-voxel proton MR spectroscopy for differentiating intracranial meningiomas from intracranial enhanced mass lesions

Background

Proton magnetic resonance spectroscopy (MRS) provides structural and metabolic information that is useful for the diagnosis of meningiomas with atypical radiological appearance. However, the metabolite that should be prioritized for the diagnosis of meningiomas has not been established.

Purpose

To evaluate the differences between the metabolic peaks of meningiomas and other intracranial enhanced mass lesions (non-meningiomas) using MR spectroscopy in short echo time (TE) spectra and the most useful metabolic peak for discriminating between the groups.

Material and Methods

The study involved 9 meningiomas, 22 non-meningiomas, intracranial enhancing tumors and abscesses, and 15 normal controls. The ranking of the peak at 3.8 ppm, peak at 3.8 ppm/Creatine (Cr), β-γ Glutamine-Glutamate (bgGlx)/Cr, N-acetyl compounds (NACs)/Cr, choline (Cho)/Cr, lipid and/or lactate (Lip-Lac) at 1.3 ppm/Cr, and the presence of alanine (Ala) were derived. The metabolic peaks were compared using the Mann-Whitney U test. ROC analysis was used to determine the cut-off values for differentiating meningiomas from non-meningiomas using statistically significant metabolic peaks.

Results

The ranking of the peak at 3.8 ppm among all the peaks, peak at 3.8 ppm/Cr, bgGlx/Cr, Lip-Lac/Cr, and the presence of Ala discriminated meningiomas from non-meningiomas with moderate to high accuracy. The highest accuracy was 96.9% at a threshold value of 3 for the rank of the peak at 3.8 ppm.

Conclusion

A distinct elevated peak at 3.8 ppm, ranked among the top three highest peaks, allowed the detection of meningiomas.

MRI predictors for brain invasion in meningiomas

BACKGROUND AND PURPOSE

In the 2016 revision of the World Health Organization classification of central nervous system tumours, brain invasion was added as an independent histological criterion for the diagnosis of a World Health Organization grade II atypical meningioma. The aim of this study was to assess whether magnetic resonance imaging characteristics can predict brain invasion for meningiomas.

MATERIALS AND METHODS

We conducted a retrospective review of all meningiomas resected at our institution between 2005 and 2016 which had preoperative magnetic resonance imaging and included brain tissue within the pathology specimen. One hundred meningiomas were included in the study, 60 of which had histopathological brain invasion, 40 of which did not. Magnetic resonance imaging characteristics of tumours were evaluated for potential predictors of brain invasion. Tumour location, size, perilesional oedema, contour, cerebrospinal fluid cleft, peritumoral cyst, dural venous sinus invasion, bone invasion, hyperostosis and the presence of enlarged pial arteries and veins were evaluated. Data were analysed using conventional chi-square, Fisher's exact test and logistic regression.

RESULTS

The volume of peritumoral oedema was significantly higher in the brain-invasive meningioma group compared to the non-brain-invasive group. The presence of a complete cleft was a rare finding that was only found in non-brain-invasive meningiomas. The presence of enlarged pial feeding arteries was a rare finding that was only found in brain-invasive meningiomas.

CONCLUSIONS

An increased volume of perilesional oedema is associated with the likelihood of brain invasion for meningiomas.

Brain invasion in meningiomas-clinical considerations and impact of neuropathological evaluation: a systematic review

With the release of the 2016 edition of the World Health Organization (WHO) Classification of Central Nervous System Tumors, brain invasion in meningiomas has been added as a stand-alone criterion for atypia and can therefore impact grading and indirectly adjuvant therapy. Regarding this rising clinical importance, we have reviewed the current knowledge about brain invasion with emphasis on its implications on current and future clinical practice. We found various definitions of brain invasion and approaches for evaluation in surgically obtained specimens described over the past decades. This heterogeneity is reflected by weak correlation with prognosis and remains controversial. Similarly, associated clinical factors are largely unknown. Preoperative, imaging-guided detection of brain invasion is unspecific, and intraoperative assessment using standard and new high-magnification microscopic techniques remains imprecise. Despite the increasing knowledge about molecular alterations of the tumor/ brain surface, pharmacotherapeutic options targeting brain invasive meningiomas are lacking. Finally, we summarize the impact of brain invasion on histopathological grading in the WHO classifications of brain tumors since 1979.In conclusion, standardized neurosurgical sampling and neuropathological analyses could improve diagnostic reliability and reproducibility of future studies. Further research is needed to improve pre- and intraoperative visualization of brain invasion and to develop adjuvant, targeted therapies.

Differentiating meningioma grade by imaging features on magnetic resonance imaging

Atypical meningioma has an aggressive clinical course. Distinguishing atypical from benign meningioma preoperatively could affect surgical planning and improve treatment outcomes. In this study, we examined whether pre-operative magnetic resonance imaging (MRI) features could distinguish between benign and atypical meningioma. Imaging factors analyzed included peritumoral edema, the presence of a draining vein, tumor necrosis, tumor location and tumor volume. Using univariate analysis, the most striking predictor of grade was tumor volume (p < .001). When adjusting for the degree of peritumoral edema, volume remained a positive predictor of higher histological grade meningioma (p = .042) and was the strongest single predictor of higher-grade meningioma in this study. Additional imaging features associated with increased risk for atypical pathology in univariate analysis included the presence of tumor necrosis (p = .012), peritumoral edema (p = .022) and location along the falx and convexity (p = .026). Despite statistically significant associations using univariate analysis, in multivariate analysis, we found that only presence of peritumoral edema was predictive of a higher-grade meningioma. Further multivariate analyses suggests that edema, draining vein and necrosis are all positive predictors of tumor volume (p < .0001). Overall, these data suggest that radiographic features including presence of tumor necrosis, and tumor location along the falx or convexity may be predictive of higher-grade meningioma when considered alone. However, most strikingly, our data point to tumor volume as the most robust pre-operative indicator of higher-grade meningioma.

Radiographic prediction of meningioma grade by semantic and radiomic features

OBJECTIVES

The clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making.

METHODS

A total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44).

RESULTS

Twelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62-0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84).

CONCLUSIONS

We found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.

MR elastography of the brain and its application in neurological diseases

Magnetic resonance elastography (MRE) is an imaging technique for noninvasively and quantitatively assessing tissue stiffness, akin to palpation. MRE is further able assess the mechanical properties of tissues that cannot be reached by hand including the brain. The technique is a three-step process beginning with the introduction of shear waves into the tissue of interest by applying an external vibration. Next, the resulting motion is imaged using a phase-contrast MR pulse sequence with motion encoding gradients that are synchronized to the vibration. Finally, the measured displacement images are mathematically inverted to compute a map of the estimated stiffness. In the brain, the technique has demonstrated strong test-retest repeatability with typical errors of 1% for measuring global stiffness, 2% for measuring stiffness in the lobes of the brain, and 3-7% for measuring stiffness in subcortical gray matter. In healthy volunteers, multiple studies have confirmed that stiffness decreases with age, while more recent studies have demonstrated a strong relationship between viscoelasticity and behavioral performance. Furthermore, several studies have demonstrated the sensitivity of brain stiffness to neurodegeneration, as stiffness has been shown to decrease in multiple sclerosis and in several forms of dementia. Moreover, the spatial pattern of stiffness changes varies among these different classes of dementia. Finally, MRE is a promising tool for the preoperative assessment of intracranial tumors, as it can measure both tumor consistency and adherence to surrounding tissues. These factors are important predictors of surgical difficulty. In brief, MRE demonstrates potential value in a number of neurological diseases. However, significant opportunity remains to further refine the technique and better understand the underlying physiology.

Meningioma consistency prediction utilizing tumor to cerebellar peduncle intensity on T2-weighted magnetic resonance imaging sequences: TCTI ratio

OBJECTIVE

Meningioma consistency, firmness or softness as it relates to resectability, affects the difficulty of surgery and, to some degree, the extent of resection. Preoperative knowledge of tumor consistency would affect preoperative planning and instrumentation. Several methods of prediction have been proposed, but the majority lack objectivity and reproducibility or generalizability to other surgeons. In a previous pilot study of 20 patients the authors proposed a new method of prediction based on tumor/cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios in comparison with objective intraoperative findings. In the present study they sought validation of this method.

METHODS

Magnetic resonance images from 100 consecutive patients undergoing craniotomy for meningioma resection were evaluated preoperatively. During surgery a consistency grade was prospectively applied to lesions by the operating surgeon, as determined by suction and/or cavitron ultrasonic surgical aspirator (CUSA) intensity. Consistency grades were A, soft; B, intermediate; and C, fibrous. Using T2-weighted MRI sequences, TCTI ratios were calculated. Analysis of the TCTI ratios and intraoperative tumor consistency was completed with ANOVA and receiver operating characteristic curves.

RESULTS

Of the 100 tumors evaluated, 50 were classified as soft, 29 as intermediate, and 21 as firm. The median TCTI ratio for firm tumors was 0.88; for intermediate tumors, 1.5; and for soft tumors, 1.84. One-way ANOVA comparing TCTI ratios for these groups was statistically significant (p < 0.0001). A single cutoff TCTI value of 1.41 for soft versus firm tumors was found to be 81.9% sensitive and 84.8% specific.

CONCLUSIONS

The authors propose this T2-based method of tumor consistency prediction with correlation to objective intraoperative consistency. This method is quantifiable and reproducible, which expands its usability. Additionally, it places tumor consistency on a graded continuum in a clinically meaningful way that could affect preoperative surgical planning.

Predicting Meningioma Consistency on Preoperative Neuroimaging Studies

This article provides an overview of the neuroimaging literature focused on preoperative prediction of meningioma consistency. A validated, noninvasive neuroimaging method to predict tumor consistency can provide valuable information regarding neurosurgical planning and patient counseling. Most of the neuroimaging literature indicates conventional MRI using T2-weighted imaging may be helpful to predict meningioma consistency; however, further rigorous validation is necessary. Much less is known about advanced MRI techniques, such as diffusion MRI, MR elastography (MRE), and MR spectroscopy. Of these methods, MRE and diffusion tensor imaging appear particularly promising.

Intracranial Meningiomas: A 30-Year Experience and Literature Review

Intracranial meningiomas are tumors arising from the covering cells of the arachnoid layer of the dura mater or from the intraventricular choroid plexus. While mostly benign tumors, they still represent a major challenge to neurosurgeons and other medical disciplines involved in their diagnostic and therapeutic management. Although this review intends to give some state-of-the-art information from the literature, it is mainly based on personal experiences since more than 30 years caring for more than 1500 meningioma patients and point to a few new strategies to further improve on patient outcome.Diagnostics are based on magnetic resonance imaging which shows the relationship between tumor and surrounding intracranial structures, particularly the brain but also the vasculature and to some extent the cranial nerves. Furthermore, it may suggest the grading of the tumor and is very helpful in the postoperative diagnosis of complications and later follow-up course.Surgery still is the main treatment with the aim to completely remove the tumor; also in cases of recurrence, other additional options include radiotherapy and radiosurgery for incompletely removed or recurrent meningiomas. Postoperative chemotherapy has not been shown to provide substantial benefit to the patient especially in highly malignant meningiomas.All therapy options should be intended to provide the patient with the best possible functional outcome. Patients' perspective is not always equivalent to surgeons' perspectives. Neuropsychological evaluation and additional guidance of patients harboring meningiomas have proven to be important in modern neurosurgical intracranial tumor treatment. Their help beyond neurosurgical care facilitates the patients to lead an independent postoperative life.

Extra-axial brain tumors

Extra-axial brain tumors are the most common adult intracranial neoplasms and encompass a broad spectrum of pathologic subtypes. Meningiomas are the most common extra-axial brain tumor (approximately one-third of all intracranial neoplasms) and typically present as slowly growing dural-based masses. Benign meningiomas are very common, and may occasionally be difficult to differentiate from more aggressive subtypes (i.e., atypical or malignant varieties) or other dural-based masses with more aggressive biologic behavior (e.g., hemangiopericytoma or dural-based metastases). Many neoplasms that typically affect the brain parenchyma (intra-axial), such as gliomas, may also present with primary or secondary extra-axial involvement. This chapter provides a general and concise overview of the common types of extra-axial tumors and their typical imaging features.

Molecular imaging correlates of tryptophan metabolism via the kynurenine pathway in human meningiomas

BACKGROUND

Increased tryptophan metabolism via the kynurenine pathway (KP) is a key mechanism of tumoral immune suppression in gliomas. However, details of tryptophan metabolism in meningiomas have not been elucidated. In this study, we evaluated in vivo tryptophan metabolism in meningiomas and compared it with gliomas using α-[(11)C]-methyl-L-tryptophan (AMT)-PET. We also explored expression patterns of KP enzymes in resected meningiomas.

METHODS

Forty-seven patients with MRI-detected meningioma (n = 16) and glioma (n = 31) underwent presurgical AMT-PET scanning. Tumoral AMT uptake and tracer kinetic parameters (including K and k3' evaluating unidirectional uptake and trapping, respectively) were measured, correlated with meningioma grade, and compared between meningiomas and gliomas. Patterns of KP enzyme expression were assessed by immunohistochemistry in all meningiomas.

RESULTS

Meningioma grade showed a positive correlation with AMT k3' tumor/cortex ratio (r = 0.75, P = .003), and this PET parameter distinguished grade I from grade II/III meningiomas with 92% accuracy. Kinetic AMT parameters could differentiate meningiomas from both low-grade gliomas (97% accuracy by k3' ratios) and high-grade gliomas (83% accuracy by K ratios). Among 3 initial KP enzymes (indoleamine 2,3-dioxygenase 1/2, and tryptophan 2,3-dioxygenase 2 [TDO2]), TDO2 showed the strongest immunostaining, particularly in grade I meningiomas. TDO2 also showed a strong negative correlation with AMT k3' ratios (P = .001).

CONCLUSIONS

PET imaging of tryptophan metabolism can provide quantitative imaging markers for differentiating grade I from grade II/III meningiomas. TDO2 may be an important driver of in vivo tryptophan metabolism in these tumors. These results can have implications for pharmacological targeting of the KP in meningiomas.

Predicting Consistency of Meningioma by Magnetic Resonance Imaging

Objective Meningioma consistency is important because it affects the difficulty of surgery. To predict preoperative consistency, several methods have been proposed; however, they lack objectivity and reproducibility. We propose a new method for prediction based on tumor to cerebellar peduncle T2-weighted imaging intensity (TCTI) ratios. Design The magnetic resonance (MR) images of 20 consecutive patients were evaluated preoperatively. An intraoperative consistency scale was applied to these lesions prospectively by the operating surgeon based on Cavitron Ultrasonic Surgical Aspirator (Valleylab, Boulder, Colorado, United States) intensity. Tumors were classified as A, very soft; B, soft/intermediate; or C, fibrous. Using T2-weighted MR sequence, the TCTI ratio was calculated. Tumor consistency grades and TCTI ratios were then correlated. Results Of the 20 tumors evaluated prospectively, 7 were classified as very soft, 9 as soft/intermediate, and 4 as fibrous. TCTI ratios for fibrous tumors were all ≤ 1; very soft tumors were ≥ 1.8, except for one outlier of 1.66; and soft/intermediate tumors were > 1 to < 1.8. Conclusion We propose a method using quantifiable region-of-interest TCTIs as a uniform and reproducible way to predict tumor consistency. The intraoperative consistency was graded in an objective and clinically significant way and could lead to more efficient tumor resection.

Regional variations and age-related changes detected with magnetic resonance spectroscopy in the brain of healthy dogs

OBJECTIVE

To investigate age-related and regional differences in estimated metabolite concentrations in the brain of healthy dogs by means of magnetic resonance spectroscopy (MRS).

ANIMALS

15 healthy Beagles.

PROCEDURES

Dogs were grouped according to age as young (n = 5; all dogs were 2 months old), adult (5; mean age, 4.5 years), or geriatric (5; all dogs were 12 years old). Imaging was performed by use of a 1.5-T MRI system with T1- and T2-weighted spin-echo and fluid-attenuated inversion recovery sequences. Signal intensity measurements for N-acetyl aspartate, creatine, choline, and lactate-alanine (the spectroscopic peaks associated with alanine and lactate could not be reliably differentiated) were determined with MRS, and areas under the spectroscopic peaks (representing concentration estimates) were calculated. Ratios of these metabolite values were compared among age groups and among brain regions with regression analysis.

RESULTS

The choline-to-creatine ratio was significantly higher in young dogs, compared with other age groups. The N-acetyl aspartate-to-choline ratio was significantly lower in young dogs and geriatric dogs than in adult dogs. When all age groups were considered, the choline-to-creatine ratio was significantly higher and N-acetyl aspartate-to-choline ratio was significantly lower in the frontal lobe than in all other regions. The N-acetyl aspartate-to-creatine ratio was significantly lower in the cerebellum than in other regions.

CONCLUSIONS AND CLINICAL RELEVANCE

Metabolite ratios varied significantly among age groups and brain regions in healthy dogs. Future studies should evaluate absolute concentration differences in a larger number of dogs and assess clinical applications in dogs with neurologic diseases.

Differential diagnosis of intracranial meningiomas based on magnetic resonance spectroscopy

BACKGROUND AND PURPOSE

To determine in vivo magnetic resonance spectroscopy (MRS) characteristics of intracranial meningiomas and to assess MRS reliability in meningioma grading and discrimination from tumours of similar radiological appearance, such as lymphomas, schwannomas and haemangiopericytomas.

MATERIAL AND METHODS

Analysis of spectra of 14 patients with meningiomas, 6 with schwannomas, 2 with lymphomas, 2 with haemangiopericytomas and 17 control spectra taken from healthy hemispheres.

RESULTS

All the patients with meningiomas had a high Cho signal (long TE). There were very low signals of Naa and Cr in the spectra of 10 patients. A reversed Ala doublet was seen only in 2 cases. Four patients had a negative Lac signal, whereas 3 had high Lac-Lip spectra. Twelve spectra showed high Cho signals (short TE). In one case the Cho signal was extremely low. All spectra displayed a very low Cr signal, but high Glx and Lac-Lip signals. Ala presence was found only in 3 patients. The mean Cho/Cr ratio (PRESS) was 5.97 (1.12 in normal brain, p < 0.05). Lac-Lip was present in all the meningiomas (STEAM). The Ala signal was seen only in 2 spectra with long TE and in 3 sequences of the short TE sequences. There were both β/γ-Glx and α-Glx/glutathione signals in all 14 meningiomas.

CONCLUSIONS

MRS is unable to discriminate low and high grade meningiomas. The method seems to be helpful in discriminating lymphomas (absent Glx signal), schwannomas (mI signal in the short TE sequences) and haemangiopericytomas (presence of mI band) from meningiomas.

Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI

PURPOSE

We assessed whether a high b-value DWI at b=4000s/mm(2) would discriminate the histopathological differentiation of the tumor grade of meningiomas, and also focused on the relationship between radiologic features and the tumor grade.

MATERIALS AND METHODS

We acquired DWI at 3T with b=1000 and b=4000s/mm(2) in 77 patients (42, 31 and 4 patients were WHO grades I (G1), II (G2), and III (G3), respectively). The apparent diffusion coefficient (ADC) was measured by placing multiple regions of interest (ROIs) on ADC maps. The ADC values of each tumor were determined preoperatively from several ROIs, and expressed as the minimum (ADCMIN), mean (ADCMEAN), and maximum absolute values (ADCMAX). We evaluated the relationship between ADCs and histological findings, and assessed the radiologic features such as tumor location, tumor size, presence/absence of peritumoral edema, shape of the tumor, presence/absence of bone destruction or hyperplasia, status of contrast enhancement, presence/absence of calcification and cyst.

RESULTS

ADCs of the meningiomas were inversely correlated with the histological grade of meningiomas. According to results of the discriminant analysis, the apparent log likelihood value was greatest for ADCMIN at b=4000. Furthermore, only the ADCMIN value at b=4000 was significantly correlated with the histological grade of meningiomas when we performed a multiple logistic regression analysis to identify the significant independent factors such as shape of tumor, presence/absence of bone destruction, status of contrast enhancement, presence/absence of cyst and ADCMIN at b=4000.

CONCLUSION

A meningioma with a low ADCMIN at a high b-value might imply a high-grade meningioma.

Preoperative assessment of meningioma stiffness using magnetic resonance elastography

OBJECT

The object of this study was to determine the potential of magnetic resonance elastography (MRE) to preoperatively assess the stiffness of meningiomas.

METHODS

Thirteen patients with meningiomas underwent 3D brain MRE examination to measure stiffness in the tumor as well as in surrounding brain tissue. Blinded to the MRE results, neurosurgeons made a qualitative assessment of tumor stiffness at the time of resection. The ability of MRE to predict the surgical assessment of stiffness was tested using a Spearman rank correlation.

RESULTS

One case was excluded due to a small tumor size. In the remaining 12 cases, both tumor stiffness alone (p = 0.023) and the ratio of tumor stiffness to surrounding brain tissue stiffness (p = 0.0032) significantly correlated with the surgeons' qualitative assessment of tumor stiffness. Results of the MRE examination provided a stronger correlation with the surgical assessment of stiffness compared with traditional T1- and T2-weighted imaging (p = 0.089), particularly when considering meningiomas of intermediate stiffness.

CONCLUSIONS

In this cohort, preoperative MRE predicted tumor consistency at the time of surgery. Tumor stiffness as measured using MRE outperformed conventional MRI because tumor appearance on T1- and T2-weighted images could only accurately predict the softest and hardest meningiomas.

(1)H MR Spectroscopy of Meningiomas at 3.0T: the Role of Glutamate-Glutamine Complex and Glutathione

Proton magnetic resonance spectroscopy ((1)H MRS) has been used extensively for the characterization of the intracranial meningiomas. A major emphasis is placed on identification of an alanine (Ala) content within these tumors. Less attention is given to other metabolites such as glutamine and glutamate (Glx). Our objective was to assess the incidence and the relevance of the Glx content in meningiomas, to evaluate their usefulness versus Ala in the diagnosis of intracranial meningiomas and to indicate a potential role of other biochemical compounds such as glutathione (GSH). We performed a retrospective review of the (1)H MRS spectra at 3.0T of 16 intracranial meningiomas in 16 consecutive patients with newly diagnosed tumors. All meningiomas were evaluated with single- voxel (1)H MRS at short echo time using an automatic quantitation of the metabolites by linear combination model (LCModel) fitting. Detailed analysis of the spectra showed that the Glx content was a more common result (100%) than the Ala content (44%). The Glx content can be considered in high concentrations within these tumors resulting in overall levels comparable to normal brain values (P > 0.2). A glutathione (GSH) spectrum was added to the LCModel basis set in six meningiomas and in all of them a GSH peak was detected at 2.95 ppm (100%). Other metabolites such as guanidinoacetate (Gua) were detected in six meningiomas (38%) and this was not reported previously. Our data indicate that Glx and GSH are far more likely to be biochemical predictors than Ala in the (1)H MRS evaluation of intracranial meningiomas. The significance of Gua as another potential marker of the meningioma cell metabolism needs to be further investigated.

Clinical characteristics of meningiomas assessed by ¹¹C-methionine and ¹⁸F-fluorodeoxyglucose positron-emission tomography

The clinical course of meningioma varies from case to case, despite similar characteristics on magnetic resonance (MR) imaging. Functional imaging including (11)C-methionine and (18)F-fluorodeoxyglucose (FDG) positron-emission tomography (PET) has been widely studied for noninvasive preoperative evaluation of brain tumors. However, few reports have examined correlations between meningiomas and findings on (11)C-methionine and FDG PET. The objective of this study was to clarify the relationship between tumor characteristics and (11)C-methionine and FDG uptake in meningiomas. For 68 meningiomas in 51 cases, (11)C-methionine uptake was evaluated by measuring both mean and maximum tumor/normal (T/N) ratio for the whole area of the tumors. FDG uptake in 44 of those meningiomas was also analyzed. Tumor size was measured volumetrically, and tumor-doubling time was estimated. Histopathological evaluation was performed in 19 surgical cases. Mean and maximum T/N ratios of (11)C-methionine PET were significantly higher in skull-base lesions than in non-skull-base lesions. Correlations of mean and maximum T/N ratio of (11)C-methionine PET with tumor-doubling time, MIB-1 labeling index, microvessel density and World Health Organization grading were not significant. Mean T/N ratio of (11)C-methionine PET correlated significantly with tumor volume according to logarithm regression modeling (P < 0.0001, R = 0.544). However, mean and maximum T/N ratio of FDG-PET correlated with none of the tumor characteristics described above. These results suggest that (11)C-methionine uptake correlates with tumor volume, but not with tumor aggressiveness.