Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging

The role of apparent diffusion coefficient in the grading of adult isocitrate dehydrogenase-mutant astrocytomas: relationship with the Ki-67 proliferation index

BACKGROUND

The grading of adult isocitrate dehydrogenase (IDH)-mutant astrocytomas is a crucial prognostic factor.

PURPOSE

To investigate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) in the grading of adult IDH-mutant astrocytomas, and to analyze the correlation between ADC and the Ki-67 proliferation index.

MATERIAL AND METHODS

The clinical and MRI data of 82 patients with adult IDH-mutant astrocytoma who underwent surgical resection and molecular genetic testing with IDH and 1p/19q were retrospectively analyzed. The conventional MRI features, ADCmin, ADCmean, and nADC of the tumors were compared using the Kruskal-Wallis single factor ANOVA and chi-square tests. Receiver operating characteristic (ROC) curves were drawn to evaluate conventional MRI and ADC accuracy in differentiating tumor grades. Pearson correlation analysis was performed to determine the correlation between ADC and the Ki-67 proliferation index.

RESULTS

The difference in enhancement, ADCmin, ADCmean, and nADC among WHO grade 2, 3, and 4 tumors was statistically significant (all P <0.05). ADCmin showed the preferable diagnostic accuracy for grading WHO grade 2 and 3 tumors (AUC=0.724, sensitivity=63.4%, specificity=80%, positive predictive value (PPV)=62.0%; negative predictive value (NPV)=82.5%), and distinguishing grade 3 from grade 4 tumors (AUC=0.764, sensitivity=70%, specificity=76.2%, PPV=75.0%, NPV=71.4%). Enhancement + ADC model showed an optimal predictive accuracy (grade 2 vs. 3: AUC = 0.759; grade 3 vs. 4: AUC = 0.799). The Ki-67 proliferation index was negatively correlated with ADCmin, ADCmean, and nADC (all P <0.05), and positively correlated with tumor grade.

CONCLUSION

Conventional MRI features and ADC are valuable to predict pathological grading of adult IDH-mutant astrocytomas.

Differential diagnosis of atypical and anaplastic meningiomas based on conventional MRI features and ADC histogram parameters using a logistic regression model nomogram

The purpose of the study was to determine the value of a logistic regression model nomogram based on conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) histogram parameters in differentiating atypical meningioma (AtM) from anaplastic meningioma (AnM). Clinical and imaging data of 34 AtM and 21 AnM diagnosed by histopathology were retrospectively analyzed. The whole tumor delineation along the tumor edge on ADC images and ADC histogram parameters were automatically generated and comparisons between the two groups using the independent samples t test or Mann-Whitney U test. Univariate and multivariate logistic regression analyses were used to construct the nomogram of the AtM and AnM prediction model, and the model's predictive efficacy was evaluated using calibration and decision curves. Significant differences in the mean, enhancement, perc.01%, and edema were noted between the AtM and AnM groups (P < 0.05). Age, sex, location, necrosis, shape, max-D, variance, skewness, kurtosis, perc.10%, perc.50%, perc.90%, and perc.99% exhibited no significant differences (P > 0.05). The mean and enhancement were independent risk factors for distinguishing AtM from AnM. The area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the nomogram were 0.871 (0.753-0.946), 80.0%, 81.0%, 79.4%, 70.8%, and 87.1%, respectively. The calibration curve demonstrated that the model's probability to predict AtM and AnM was in favorable agreement with the actual probability, and the decision curve revealed that the prediction model possessed satisfactory clinical availability. A logistic regression model nomogram based on conventional MRI features and ADC histogram parameters is potentially useful as an auxiliary tool for the preoperative differential diagnosis of AtM and AnM.

Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index

This study aims to investigate the predictive value of preoperative whole-tumor histogram analysis of multi-parametric MRI for histological subtypes in patients with lung cancer brain metastases (BMs) and explore the correlation between histogram parameters and Ki-67 proliferation index. The preoperative MRI data of 95 lung cancer BM lesions obtained from 73 patients (42 men and 31 women) were retrospectively analyzed. Multi-parametric MRI histogram was used to distinguish small-cell lung cancer (SCLC) from non-small cell lung cancer (NSCLC), and adenocarcinoma (AC) from squamous cell carcinoma (SCC), respectively. The T1-weighted contrast-enhanced (T1C) and apparent diffusion coefficient (ADC) histogram parameters of the volumes of interest (VOIs) in all BMs lesions were extracted using FireVoxel software. The following histogram parameters were obtained: maximum, minimum, mean, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, entropy, and 1st-99th percentiles. Then investigated their relationship with the Ki-67 proliferation index. The skewness-T1C, kurtosis-T1C, minimum-ADC, mean-ADC, CV-ADC and 1st - 90th ADC percentiles were significantly different between the SCLC and NSCLC groups (all p < 0.05). When the 10th-ADC percentile was 668, the sensitivity, specificity, and accuracy (90.80%, 76.70% and 86.32%, respectively) for distinguishing SCLC from NSCLC reached their maximum values, with an AUC of 0.895 (0.824 - 0.966). Mean-T1C, CV-T1C, skewness-T1C, 1st - 50th T1C percentiles, maximum-ADC, SD-ADC, variance-ADC and 75th - 99th ADC percentiles were significantly different between the AC and SCC groups (all p < 0.05). When the CV-T1C percentiles was 3.13, the sensitivity, specificity and accuracy (75.00%, 75.60% and 75.38%, respectively) for distinguishing AC and SCC reached their maximum values, with an AUC of 0.829 (0.728-0.929). The 5th-ADC and 10th-ADC percentiles were strongly correlated with the Ki-67 proliferation index in BMs. Multi-parametric MRI histogram parameters can be used to identify the histological subtypes of lung cancer BMs and predict the Ki-67 proliferation index.

T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma

OBJECTIVE

To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma.

METHODS

This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman's rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression.

RESULTS

High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 (p = 0.001-0.009), lower minimum, and C10 of ADC (p = 0.013-0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance).

CONCLUSION

T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

KEY POINTS

• The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.

Role of diffusion-weighted imaging in differentiating angiomatous meningioma from atypical meningioma

BACKGROUND

Conventional magnetic resonance imaging (MRI) characteristics of angiomatous meningioma (AM) and atypical meningioma (ATM) sometimes overlap. However, there are significant differences in the treatment and prognosis of the two tumors. The aim of the study was to assess the role of diffusion-weighted imaging (DWI) in differentiating AM from ATM.

METHODS

Clinical, MRI, and pathological data of 25 patients with AM and 30 patients with ATM were retrospectively analyzed. Main clinical indexes, conventional MRI characteristics, and apparent diffusion coefficient (ADC) values were compared between the two groups, and receiver operating characteristic (ROC) curves were drawn to determine the diagnostic performance of ADC values in distinguishing AM from ATM.

RESULTS

The minimum ADC value (ADCmin), average ADC value (ADCmean), and relative ADC value (rADC) for AM (908.00 ± 117.00 × 10^-6 mm²/s, 921.04 ± 67.09 × 10^-6 mm²/s, and 1.15 ± 0.09, respectively) were significantly higher than those for ATM (710.50 ± 79.80 × 10^-6 mm²/s, 748.50 ± 67.27 × 10^-6 mm²/s, and 0.96 ± 0.09, respectively; all P < 0.05). ROC analysis showed that ADCmin had the best diagnostic performance in distinguishing AM from ATM, with an area under the curve value of 0.977. When using 759.00 × 10^-6 mm²/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 90.00 %, 96.00 %, 92.72 %, 96.40 %, and 88.90 %, respectively.

CONCLUSIONS

DWI plays an important role in differentiating AM from ATM, and ADCmin is the most promising potential parameter that can improve the preoperative diagnostic accuracy of both tumors.

Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis

This study aimed to investigate the value of apparent diffusion coefficient (ADC) histogram analysis in differentiating intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) from atypical meningioma (ATM). Retrospective analyzed the clinical, magnetic resonance imaging, and pathological data of 20 and 25 patients with SFT/HPC and ATM, respectively. Histogram analysis was performed on the axial ADC images using MaZda software, and nine histogram parameters were obtained, including mean, variance, skewness, kurtosis, and the 1^st (ADC1), 10^th (ADC10), 50^th (ADC50), 90^th (ADC90), and 99^th (ADC99) percentile ADC. Differences in ADC histogram parameters between SFT/HPC and ATM were compared by an independent t test or Mann-Whitney U test, while the statistically significant histogram parameters were further analyzed by drawing receiver operating characteristic (ROC) curves to evaluate the differential diagnostic performance. Among the nine ADC histogram parameters we extracted, the mean, ADC1, ADC10, ADC50, and ADC90 in the SFT/HPC group were greater than those of ATM, and significant differences were observed (all P < 0.05). ROC analysis showed that the ADC1 generated the highest area under the curve (AUC) value of 0.920 in distinguishing the two tumors, when using 91.00 as the optimal threshold. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value in distinguishing between SFT/HPC and ATM were 84.00%, 85.00%, 84.44%, 87.50%, and 81.00%, respectively. ADC histogram analysis can be a reliable tool to differentiate between SFT/HPC and ATM, with the ADC1 being the most promising potential parameter.

Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type

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Tensor-valued dMRI facilitates prediction of meningioma consistency, grade and type.

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Tensor-valued dMRI corroborates findings of diffusion tensor and kurtosis imaging.

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MK and MK_A is associated with firm and MD with variable meningioma consistency.

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Variability of MK_I in the vicinity of the tumor is associated with meningioma grade.

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MK_{A 50} and MK_{I 50} separates psammomatous meningiomas from other meningioma types.

Background

Preoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach.

Purpose

To investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type.

Materials and methods

30 patients with intracranial meningiomas (22 WHO grade I, 8 WHO grade II) underwent MRI prior to surgery. Diffusion MRI was performed with linear and spherical b-tensors with b-values up to 2000 s/mm². The data were used to estimate mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and its components—the anisotropic and isotropic kurtoses (MK_A and MK_I). Meningioma consistency was estimated for 16 patients during resection based on ultrasonic aspiration intensity, ease of resection with instrumentation or suction. Grade and type were determined by histopathological analysis. The relation between consistency, grade and type and dMRI parameters was analyzed inside the tumor (“whole-tumor”) and within brain tissue in the immediate periphery outside the tumor (“rim”) by histogram analysis.

Results

Lower 10th percentiles of MK and MK_A in the whole-tumor were associated with firm consistency compared with pooled soft and variable consistency (n = 7 vs 9; U test, p = 0.02 for MK_{A 10} and p = 0.04 for MK₁₀) and lower 10th percentile of MD with variable against soft and firm (n = 5 vs 11; U test, p = 0.02). Higher standard deviation of MK_I in the rim was associated with lower grade (n = 22 vs 8; U test, p = 0.04) and in the MK_I maps we observed elevated rim-like structure that could be associated with grade. Higher median MK_A and lower median MK_I distinguished psammomatous type from other pooled meningioma types (n = 5 vs 25; U test; p = 0.03 for MK_{A 50} and p = 0.03 and p = 0.04 for MK_{I 50}).

Conclusion

Parameters from tensor-valued dMRI can facilitate prediction of consistency, grade and type.

Apparent Diffusion Coefficient Histogram Analysis for the Preoperative Evaluation of Ki-67 Expression in Pituitary Macroadenoma

PURPOSE

To explore the value of an apparent diffusion coefficient (ADC) histogram in predicting the Ki-67 proliferation index in pituitary macroadenomas.

MATERIAL AND METHODS

This retrospective study analyzed the pathological and imaging data of 102 patients with pathologically confirmed pituitary macroadenoma. Immunohistochemistry staining was used to assess Ki-67 expression in tumor tissue samples, and a high Ki-67 labeling index was defined as 3%. The ADC images of the maximum slice of tumors were selected and the region of interest (ROI) of each slice was delineated using the MaZda software (version 4.7, Technical University of Lodz, Institute of Electronics, Łódź, Poland) and analyzed by ADC histogram. Histogram characteristic parameters were compared between the high Ki-67 group (n = 42) and the low Ki-67 group (n = 60). The important parameters were further analyzed by receiver operating characteristic (ROC).

RESULTS

The mean value, and the 1st, 10th, 50th, 90th, and 99th percentiles were found to be negatively correlated with Ki-67 expression (all P < 0.05), with correlation coefficients of -0.292, -0.352, -0.344, -0.289, -0.253 and -0.267, respectively. The mean ADC and the 1st, 10th, 50th, 90th, and 99th quantiles extracted from the histogram were significantly lower in the high Ki-67 group than in the low Ki-67 group (all P < 0.05). The area under the ROC curve was 0.699-0.720; however, there were no significant between-group differences in variance, skewness and kurtosis (all P > 0.05).

CONCLUSION

An ADC histogram can be a reliable tool to predict the Ki-67 proliferation status in patients with pituitary macroadenomas.

Differentiating atypical meningioma from anaplastic meningioma using diffusion weighted imaging

PURPOSE

To explore the value of MRI conventional features and apparent diffusion coefficient (ADC) on the differential diagnosis of atypical meningioma (AtM) and anaplastic meningioma (AnM).

MATERIALS AND METHODS

This retrospective study analyzed the preoperative clinical data, MRI conventional features, and DWI data of 55 AtM and 25 AnM confirmed by pathology in our hospital. The clinical features, MRI conventional features, ADCmean, ADCmin, and relative ADC (rADC) values were compared between the two tumors by Chi-square test or an independent sample t-test. Receiver operating characteristic curve (ROC) and binary logistic regression analysis were used to evaluate the diagnostic efficacy of each parameter to differentiate between these tumors.

RESULTS

The MRI conventional features had a certain ability to distinguish AnM and AtM, with an area under the curve value (AUC) of 0.824 (95% CI, 0.723-0.900). The ADCmean, ADCmin, and rADC values were significantly higher in AtM compared to AnM (all P < 0.05). ADCmean had the best identification effect with an AUC of 0.867 (95% CI, 0.772-0.933) among them, at an cut-off of 0.817 × 10^-3 mm²/s, the sensitivity and specificity of distinguishing AtM from AnM were 78.18% and 88.00%, respectively. A combination of ADCmean and MRI conventional features showed the optimum discrimination ability for the two tumors, the AUC, sensitivity, specificity, and accuracy were 0.918 (95% CI, 0.835-0.967), 80.00%, 94.55%, and 90.00%, respectively.

CONCLUSION

MRI conventional features combined with ADCmean, as a non-invasive method, has potential clinical value in the preoperative diagnosis of AtM and AnM.

Are the clinical manifestations of CT scan and location associated with World Health Organization histopathological grades of meningioma?: A retrospective study

Introduction

meningioma is the most common intracranial tumor. CT scan is a common method for diagnosis. WHO classified meningioma into 3 histological grades? This study aims to evaluate the relation of different meningioma signs on CT and tumor distribution regard to WHO histological types.

Methods

In this single-center observational retrospective study, authors reviewed data of 75 meningioma patients confirmed by the WHO histological grades (WHO I/II/III) which were underwent CT scans from January 1, 2005 to December 30, 2019 at a teaching hospital, in XXXX. Data collected using patients medical records. Data were analyzed by SPSS 20 and P less than 0.05 was assumed as significant.

Result

Our study confirmed that only edema (P = 0.005) and heterogeneity (P = 0.014) had a significant association with malignant histological types. Other signs were not statistically different among WHO histology types (p > 0.05). On the subject of tumor location, atypical/malignant meningioma was significantly more common in parasagittal (P = 0.031) and front-parietal (P = 0.035) regions.

Discussion

meningiomas with Edema, heterogeneity on CT, and tumors located in parasagittal and frontoparietal regions are related to malignant histology and should be evaluated and treated more precisely.

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CT scan is a common method for diagnosis of meningioma.

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In This retrospective study 75 meningioma patients' data reviewed.

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CT scan signs of edema and heterogeneity had a significant association with malignant histological types.

Differentiating between non-functioning pituitary macroadenomas and sellar meningiomas using ADC

INTRODUCTION AND AIM

It is difficult to distinguish between non-functioning pituitary macroadenomas (NFPMAs) and sellar meningiomas because of their overlapping imaging manifestations on routine MRI, especially in cases of meningiomas growing into the saddle. Here, we aimed to differentiate between these two tumors using apparent diffusion coefficient (ADC) values and MRI characteristics.

METHODS

A total of 60 NFPMA and 52 sellar meningioma cases confirmed by the pathological analysis were retrospectively reviewed. All patients were examined via routine MRI and diffusion-weighted imaging (DWI) before undergoing surgery. The clinical characteristics, MRI characteristics, and max ADC (ADCmax), average ADC (ADCmean), and minimum ADC (ADCmin) values were compared between the two tumors via Chi-square test and two sample t-tests. Receiver operating characteristic (ROC) curve and binary logistic regression analyses were conducted to determine the discrimination ability.

RESULTS

The ADCmax, ADCmean, and ADCmin values were significantly higher in NFPMAs compared to sellar meningiomas (P < 0.001 for all). Among ADC values, ADCmax demonstrated good performance with an AUC of 0.896 (95% CI, 0.823-0.969) and accuracy of 88.7%. A cut-off value of 0.97 × 10-3 mm2/s was used for ADCmax for differentiation between tumors. A combination of ADCmax values and clinicoradiological features showed the best discrimination ability for differential diagnosis between the two tumors, with an AUC of 0.981 (95% CI, 0.958-1.000) and accuracy of 96.9%.

CONCLUSION

A combination of ADCmax and clinicoradiological features demonstrates good discrimination ability and high accuracy for differentiation between NFPMAs and sellar meningiomas, and is a potential quantitative tool to aid in the selection of surgical techniques.

Differentiating Giant Cell Glioblastoma from Classic Glioblastoma With Diffusion-Weighted Imaging

BACKGROUND

Differential diagnosis of giant cell glioblastoma (GC) and classic glioblastoma (GBM) using conventional radiological modalities is difficult. This study aimed to use diffusion-weighted imaging (DWI) to distinguish GC from GBM and thereby improve the accuracy of preoperative assessment of patients with GB.

METHODS

The clinical, magnetic resonance imaging, and pathologic data of 12 patients with GC and 21 patients with GBM were retrospectively analyzed. Independent sample t tests were used to compare the minimum apparent diffusion coefficient (ADC_min) and the normalized apparent diffusion coefficients (nADC) of the 2 tumor types. Receiver operating curve (ROC) analysis was used to assess the diagnostic efficacy of ADC_min and nADC values.

RESULTS

Compared with that of the classic GBM group, the ADC_min (0.98 ± 0.14 vs. 0.80 ± 0.19×10^-3 mm²/second, P = 0.007) and nADC (1.42 ± 0.25 vs. 1.17 ± 0.25, P = 0.011) of the GC group were significantly higher. ROC curve analysis showed that the maximum area under the curve of ADC_min and nADC were 0.800 ± 0.080 and 0.778 ± 0.082, respectively. The sensitivity, specificity, and accuracy distinguishing GC and classic GBM was best (83.33%, 76.19%, and 78.79%, respectively) when ADC_min = 0.84×10^-3 mm²/second (maximum area under the ROC, 0.800). Its positive and negative predictive values under this condition were 88.89% and 66.67%, respectively.

CONCLUSIONS

By distinguishing GC from classic GBM, the ADC_min parameter of DWI can improve the accuracy of the preoperative differential diagnosis of the 2 tumor types.

Apparent Diffusion Coefficient to Evaluate Adult Intracranial Ependymomas: Relationship to Ki-67 Proliferation Index

BACKGROUND AND PURPOSE

There are important differences in the treatment and prognosis of adult intracranial low-grade ependymomas (grade II) versus anaplastic ependymomas (grade III). We evaluated the value of the apparent diffusion coefficient (ADC) for differentiating these two tumors and further investigated the relationship between ADC values and the Ki-67 proliferation index.

METHODS

Clinical and preoperative magnetic resonance imaging data of 35 cases of adult intracranial ependymomas were retrospectively analyzed, including 20 low-grade ependymomas and 15 anaplastic ependymomas. The minimum ADC (ADCmin), average ADC (ADCmean), and normalized ADC (nADC) were compared between the two groups. Receiver operating characteristic curves were drawn to evaluate the differentiating accuracy of ADC values. The Ki-67 proliferation index of the solid tumor components was also measured to explore its relationship with ADC values.

RESULTS

The ADCmin (.89 ± .17 vs. .66 ± .09 × 10^-3  mm² /second), ADCmean (.98 ± .21 vs. .72 ± .11 × 10^-3 mm² /second), and nADC (1.38 ± .31 vs. 1.02 ± .18 × 10^-3  mm² /second) were significantly higher in adult intracranial low-grade ependymomas than anaplastic ependymomas cases (all P < .05). ADCmean best distinguished the two groups, with an area under the curve value of .900. Using .716 × 10^-3  mm² /second as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the two groups were 66.7%, 100%, 85.7%, 100%, and 80%, respectively. ADCmin (r = -.490), ADCmean (r = -.449), and nADC (r = -.425) showed significant negative correlations with the Ki-67 proliferation index (all P < .05).

CONCLUSIONS

ADC values can differentiate adult intracranial low-grade ependymomas and anaplastic ependymomas, which could improve the preoperative diagnostic accuracy of these two tumors and guide their treatment.