Differentiation between vestibular schwannomas and meningiomas with atypical appearance using diffusion kurtosis imaging and three-dimensional arterial spin labeling imaging

Histogram Analysis of Advanced Diffusion-weighted MRI Models for Evaluating the Grade and Proliferative Activity of Meningiomas

RATIONALE AND OBJECTIVES

To explore the value of diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) magnetic resonance imaging histogram analysis in evaluating the grade and proliferative activity of meningiomas.

MATERIALS AND METHODS

A total of 134 meningioma patients were prospectively included and underwent magnetic resonance diffusion imaging. The whole-tumor histogram parameters were extracted from multiple functional maps. Mann-Whitney U test was used to compare the histogram parameters of high- and low-grade meningiomas. The receiver operating characteristic (ROC) curve and multiple logistic regression analysis were used to evaluate the diagnostic efficacy. The correlation between histogram parameters and the Ki-67 index was analyzed. The diffusion model was further validated with an independently validation set (n = 33).

RESULTS

Among single histogram parameters, the variance of NODDI-ISOVF (isotropic volume fraction) showed the highest AUC of 0.829 in grading meningiomas. For the combined models, the DKI model had the best performance in the diagnosis (AUC=0.925). Delong test showed the DKI combined model showed superior diagnostic performance to those of DTI, NODDI and MAP models (P < 0.05 for all). Moreover, moderate to weak correlations were found between various diffusion parameters and the Ki-67 labeling index (rho=0.20-0.45, P < 0.05 for all). In the validation set, the DKI model still showed higher performance (AUC, 0.85) than other diffusion models, thus demonstrating robustness.

CONCLUSIONS

Whole-tumor histogram analyses of DTI, DKI, NODDI, and MAP are useful for evaluating the grade and cellular proliferation of meningiomas. DKI combined model has higher diagnostic accuracy than DTI, NODDI and MAP in meningioma grading.

Histogram analysis of mono-exponential, bi-exponential and stretched-exponential diffusion-weighted MR imaging in predicting consistency of meningiomas

BACKGROUND

The consistency of meningiomas is critical to determine surgical planning and has a significant impact on surgical outcomes. Our aim was to compare mono-exponential, bi-exponential and stretched exponential MR diffusion-weighted imaging in predicting the consistency of meningiomas before surgery.

METHODS

Forty-seven consecutive patients with pathologically confirmed meningiomas were prospectively enrolled in this study. Two senior neurosurgeons independently evaluated tumour consistency and classified them into soft and hard groups. A volume of interest was placed on the preoperative MR diffusion images to outline the whole tumour area. Histogram parameters (mean, median, 10th percentile, 90th percentile, kurtosis, skewness) were extracted from 6 different diffusion maps including ADC (DWI), D*, D, f (IVIM), alpha and DDC (SEM). Comparisons between two groups were made using Student's t-Test or Mann-Whitney U test. Parameters with significant differences between the two groups were included for Receiver operating characteristic analysis. The DeLong test was used to compare AUCs.

RESULTS

DDC, D* and ADC 10th percentile were significantly lower in hard tumours than in soft tumours (P ≤ 0.05). The alpha 90th percentile was significantly higher in hard tumours than in soft tumours (P < 0.02). For all histogram parameters, the alpha 90th percentile yielded the highest AUC of 0.88, with an accuracy of 85.10%. The D* 10th percentile had a relatively higher AUC value, followed by the DDC and ADC 10th percentile. The alpha 90th percentile had a significantly greater AUC value than the ADC 10th percentile (P ≤ 0.05). The D* 10th percentile had a significantly greater AUC value than the ADC 10th percentile and DDC 10th percentile (P ≤ 0.03).

CONCLUSION

Histogram parameters of Alpha and D* may serve as better imaging biomarkers to aid in predicting the consistency of meningioma.

Anatomy and Pathology of the Skull Base: Malignant and Nonmalignant Lesions

The skull base (SB) is the osseous foundation of the cranial vault. It contains many openings that allow communication between the extracranial and intracranial structures. This communication is crucial in normal physiologic processes yet may also arrow spread of disease. This article provides a comprehensive review of SB anatomy including important landmarks and anatomic variants relevant to SB surgery. We also illustrate the diverse pathologies affecting the SB.

Three-dimensional arterial spin labeling and diffusion kurtosis imaging in evaluating perfusion and infarct area size in acute cerebral ischemia

BACKGROUND

Early thrombolytic therapy is crucial to treat acute cerebral infarction, especially since the onset of thrombolytic therapy takes 1-6 h. Therefore, early diagnosis and evaluation of cerebral infarction is important.

AIM

To investigate the diagnostic value of magnetic resonance multi-delay three-dimensional arterial spin labeling (3DASL) and diffusion kurtosis imaging (DKI) in evaluating the perfusion and infarct area size in patients with acute cerebral ischemia.

METHODS

Eighty-four patients who experienced acute cerebral ischemia from March 2019 to February 2021 were included. All patients in the acute stage underwent magnetic resonance-based examination, and the data were processed by the system’s own software. The apparent diffusion coefficient (ADC), average diffusion coefficient (MD), axial diffusion (AD), radial diffusion (RD), average kurtosis (MK), radial kurtosis (fairly RK), axial kurtosis (AK), and perfusion parameters post-labeling delays (PLD) in the focal area and its corresponding area were compared. The correlation between the lesion area of cerebral infarction under MK and MD and T2-weighted imaging (T2WI) was analyzed.

RESULTS

The DKI parameters of focal and control areas in the study subjects were compared. The ADC, MD, AD, and RD values in the lesion area were significantly lower than those in the control area. The MK, RK, and AK values in the lesion area were significantly higher than those in the control area. The MK/MD value in the infarct lesions was used to determine the matching situation. MK/MD < 5 mm was considered matching and MK/MD ≥ 5 mm was considered mismatching. PLD1.5s and PLD2.5s perfusion parameters in the central, peripheral, and control areas of the infarct lesions in MK/MD-matched and -unmatched patients were not significantly different. PLD1.5s and PLD2.5s perfusion parameter values in the central area of the infarct lesions in MK/MD-matched and -unmatched patients were significantly lower than those in peripheral and control areas. The MK and MD maps showed a lesion area of 20.08 ± 5.74 cm² and 22.09 ± 5.58 cm², respectively. T2WI showed a lesion area of 19.76 ± 5.02 cm². There were no significant differences in the cerebral infarction lesion areas measured using the three methods. MK, MD, and T2WI showed a good correlation.

CONCLUSION

DKI parameters showed significant difference between the focal and control areas in patients with acute ischemic cerebral infarction. 3DASL can effectively determine the changes in perfusion levels in the lesion area. There was a high correlation between the area of the infarct lesions diagnosed by DKI and T2WI.

Noncontrast Pediatric Brain Perfusion: Arterial Spin Labeling and Intravoxel Incoherent Motion

Noncontrast magnetic resonance imaging techniques for measuring brain perfusion include arterial spin labeling (ASL) and intravoxel incoherent motion (IVIM). These techniques provide noninvasive and repeatable assessment of cerebral blood flow or cerebral blood volume without the need for intravenous contrast. This article discusses the technical aspects of ASL and IVIM with a focus on normal physiologic variations, technical parameters, and artifacts. Multiple pediatric clinical applications are presented, including tumors, stroke, vasculopathy, vascular malformations, epilepsy, migraine, trauma, and inflammation.

Intracranial Malignant Triton Tumor in Children: A Case Report

: Malignant triton tumor (MTT) is a rare, aggressive type of tumor that commonly originates from mesenchymal tissue and is mainly found in adults. Herein, we report a single case of a 12-year-old boy diagnosed with intracranial MTT. This report presents the clinical features and imaging findings of MTT. The 12-year-old patient consulted the hospital due to intermittent dizziness and headache. Computed tomography (CT) showed low-density space-occupying lesion in the left parietal fossa. Magnetic resonance imaging (MRI) showed a mass shadow with slightly long T1 and long T2 signal intensity in the same area. Contrast enhanced MRI (CE-MRI) showed obvious enhancement of the lesion. He was diagnosed with meningioma and underwent surgery. Postoperative histopathological examination diagnosed the lesion as MTT. Two months after the operation, CT examination showed tumor recurrence. He then underwent local radiotherapy and chemotherapy, and unfortunately, died six months later. Intracranial MTT should be considered as one of the differential diagnoses of intracranial meningiomas. CT and MRI are of great significance in the identification of lesion location, invasion range, and degree of malignancy. Final diagnosis of intracranial MTT requires histopathological examination. MTT has a poor prognosis, and surgical resection of the tumor is the preferred treatment. Intervention after early diagnosis is the key to improve the outcome of patients.

Can Arterial Spin Labeling Perfusion Imaging be Used to Differentiate Nasopharyngeal Carcinoma From Nasopharyngeal Lymphoma?

BACKGROUND

Differentiating nasopharyngeal carcinoma (NPC) from nasopharyngeal lymphoma (NPL) is useful for deciding the appropriate treatment. However, the diagnostic accuracy of current imaging methods is low.

PURPOSE

To explore the feasibility of arterial spin labeling (ASL) perfusion imaging in the qualitative and quantitative differentiation between NPC and NPL to improve the diagnosis of malignancies in the nasopharynx.

STUDY TYPE

Retrospective.

POPULATION

Ninety seven patients: NPC (65 cases) and NPL (32 cases), histologically confirmed.

FIELD STRENGTH/SEQUENCE

3T/3D fast spin echo pseudo-continuous ASL imaging with spiral readout scheme, 3D inverse recovery_- fast spoiled gradient recalled echo brain volume (BRAVO) imaging.

ASSESSMENT

Cerebral blood flow (CBF) images from ASL perfusion imaging were assessed by three radiologists. Each tumor was visually scored based on CBF images. Intratumoral CBF and intramuscular CBF values were obtained from intratumoral and lateral pterygoid muscle areas, respectively. Through dividing intratumoral CBF by intramuscular CBF, normalized CBF (nCBF) was further calculated.

STATISTICAL TESTS

Fleiss's kappa and intraclass correlation coefficients (ICCs) were used to assess interobserver agreement among the three readers. The Mann-Whitney U-test was used to compare visual scoring, and an unpaired t-test was performed to compare CBF value between the NPC and NPL groups. The area under the curve (AUC) value was used to quantify the diagnostic ability of each parameter.

RESULTS

Good interobserver agreements were validated by high Fleiss's kappa and ICC values (all >0.80). NPCs showed significantly higher visual scores than NPLs (P < 0.05). Both intratumoral CBF and nCBF in NPC were significantly higher than those in NPL (both P < 0.05). Intratumoral CBF showed the highest AUC of 0.861 (P < 0.05) in differentiating NPC (n = 65) from NPL (n = 32), while the AUCs of nCBF and visual scoring were 0.847 and 0.753, respectively.

DATA CONCLUSION

For the diagnosis of distinguishing NPC from NPL, ASL perfusion imaging demonstrated high diagnostic efficiency.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Precise discrimination between meningiomas and schwannomas using time-to-signal intensity curves and percentage signal recoveries obtained from dynamic susceptibility perfusion imaging

BACKGROUND AND PURPOSE

Meningiomas and schwannomas are common extra-axial brain tumors. Discrimination is challenging in some locations when characteristic imaging features are absent. This study investigated the accuracy of percentage signal recoveries obtained from dynamic susceptibility contrast perfusion imaging (DSC-PI) in discriminating meningiomas and schwannomas.

MATERIAL AND METHODS

Retrospective database research was conducted. Sixty nine meningioma and 15 schwannoma having DSC-PI between January 2016 and February 2020 were included. Time to signal intensity curves (TSIC) were analyzed and grouped as T1-dominant leakage, T2*-dominant leakage and return to baseline. Relative cerebral blood volume (rCBV), relative mean transit time (rMTT), percentage signal recovery 1 (PSR 1) and PSR 2 values were calculated. The differences between the groups were investigated. Receiver operating characteristic curves were operated.

RESULTS

rCBV, rMTT, PSR 1 and PSR 2 values were statistically different between meningiomas and schwannomas. PSR 2 provided the best discrimination. With the cut off value of 1.08 for PSR 2, meningiomas and schwannomas were differentiated with 95.7% sensitivity and 93.3% specificity. TSICs were also different between two groups. Most of meningiomas showed T2*-dominant leakage (78.2%), whereas most of shwannomas showed T1-dominant leakage (93.3%).

CONCLUSION

DSC-PI is a useful imaging tool for non-invasive discrimination of meningiomas and schwannomas. Particularly, percentage signal recoveries discriminates meningiomas and schwannomas with high sensitivity and specificity.

Machine Learning in Meningioma MRI: Past to Present. A Narrative Review

Meningioma is one of the most frequent primary central nervous system tumors. While magnetic resonance imaging (MRI), is the standard radiologic technique for provisional diagnosis and surveillance of meningioma, it nevertheless lacks the prima facie capacity in determining meningioma biological aggressiveness, growth, and recurrence potential. An increasing body of evidence highlights the potential of machine learning and radiomics in improving the consistency and productivity and in providing novel diagnostic, treatment, and prognostic modalities in neuroncology imaging. The aim of the present article is to review the evolution and progress of approaches utilizing machine learning in meningioma MRI-based sementation, diagnosis, grading, and prognosis. We provide a historical perspective on original research on meningioma spanning over two decades and highlight recent studies indicating the feasibility of pertinent approaches, including deep learning in addressing several clinically challenging aspects. We indicate the limitations of previous research designs and resources and propose future directions by highlighting areas of research that remain largely unexplored. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2.

Histogram analysis in predicting the grade and histological subtype of meningiomas based on diffusion kurtosis imaging

BACKGROUND

Presurgical grading is particularly important for selecting the best therapeutic strategy for meningioma patients.

PURPOSE

To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the differentiation of grades and histological subtypes of meningiomas.

MATERIAL AND METHODS

A total of 172 patients with histopathologically proven meningiomas underwent preoperative magnetic resonance imaging (MRI) and were classified into low-grade and high-grade groups. Mean kurtosis (MK), fractional anisotropy (FA), and mean diffusivity (MD) histograms were generated based on solid components of the whole tumor. The following parameters of each histogram were obtained: 10th, 25th, 75th, and 90th percentiles, mean, median, maximum, minimum, and kurtosis, skewness, and variance. Comparisons of different grades and subtypes were made by Mann-Whitney U test, Kruskal-Wallis test, ROC curves analysis, and multiple logistic regression. Pearson correlation was used to evaluate correlations between histogram parameters and the Ki-67 labeling index.

RESULTS

Significantly higher maximum, skewness, and variance of MD, mean, median, maximum, variance, 10th, 25th, 75th, and 90th percentiles of MK were found in high-grade than low-grade meningiomas (all P < 0.05). DKI histogram parameters differentiated 7/10 pairs of subtype pairs. The 90th percentile of MK yielded the highest AUC of 0.870 and was the only independent indicator for grading meningiomas. Various DKI histogram parameters were correlated with the Ki-67 labeling index (P < 0.05).

CONCLUSION

The histogram analysis of DKI is useful for differentiating meningioma grades and subtypes. The 90th percentile of MK may serve as an optimal parameter for predicting the grade of meningiomas.