Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Preoperative Radiologic Classification of Convexity Meningioma to Predict the Survival and Aggressive Meningioma Behavior

  • Yi Liu ,

    ‡ These authors are co-first contributors on this work.

    Affiliation Department of Neurosurgery, Nanfang Hospital of Southern Medical University, Guangzhou, China

  • Silky Chotai ,

    ‡ These authors are co-first contributors on this work.

    Affiliation Department of Neurosurgery, Nanfang Hospital of Southern Medical University, Guangzhou, China

  • Ming Chen ,

    ‡ These authors are co-first contributors on this work.

    Affiliation Department of Neurosurgery, Nanfang Hospital of Southern Medical University, Guangzhou, China

  • Shi Jin,

    Affiliation Department of Neurosurgery, Nanfang Hospital of Southern Medical University, Guangzhou, China

  • Song-tao Qi ,

    liuyi86818@126.com (SQ); panjundu@hotmail.com (JP)

    Affiliation Department of Neurosurgery, Nanfang Hospital of Southern Medical University, Guangzhou, China

  • Jun Pan

    liuyi86818@126.com (SQ); panjundu@hotmail.com (JP)

    Affiliation Department of Neurosurgery, Nanfang Hospital of Southern Medical University, Guangzhou, China

Abstract

Background

A subgroup of meningioma demonstrates clinical aggressive behavior. We set out to determine if the radiological parameters can predict histopathological aggressive meningioma, and propose a classification to predict survival and aggressive meningioma behavior.

Methods

A retrospective review of medical records was conducted for patients who underwent surgical resection of their convexity meningioma. WHO-2007 grading was used for histopathological diagnosis. Preoperative radiologic parameters were analyzed, each parameter was scored 0 or 1. Signal intensity on diffusion weighted MRI (DWI) (hyperintensity=1), heterogeneity on T1-weighted gadolinium enhanced MRI (heterogeneity=1), disruption of arachnoid at brain-tumor interface=1and peritumoral edema (PTE) on T2-weighted MRI (presence of PTE=1) and tumor shape (irregular shape=1). Multivariate logistic regression analyses were conducted to determine association of radiological parameters to histopathological grading. Kaplan-Meier and Cox regression models were used to determine the association of scoring system to overall survival and progression free survival (PFS). Reliability of the classification was tested using Kappa co-efficient analysis.

Results

Hyperintensity on DWI, disruption of arachnoid at brain-tumor interface, PTE, heterogenicitiy on T1-weighted enhanced MRI and irregular tumor shape were independent predictors of non-grade I meningioma. Mean follow-up period was 94.6 months (range, 12-117 months). Median survival and PFS in groups-I, II and III was 114.1±1.2 and 115.7± 0.8, 88± 3.3 and 58.5±3.9, 43.2± 5.1 and 18.2±1.7 months respectively. In cox regression analysis model, age (P<0.0001, OR–1.039, CI-1.017-0.062), WHO non-grade-I meningioma (P=0.017, OR–3.014, CI-1.217-7.465), radiological classification groups II (P=0.002, OR–6.194, CI–1.956-19.610) and III (P<0.0001, OR–21.658, CI–5.701-82.273) were independent predictors of unfavorable survival outcomes.

Conclusions

Preoperative radiological classification can be used as a supplement to the histopathological grading. Group-I meningiomas demonstrate benign radiological, histopathological and clinical features; group-III demonstrates aggressive features. Group-II meningiomas demonstrate intermediate features; the need for more aggressive follow-up and/or treatment should be further investigated.

Introduction

Meningiomas account for 20–32% of all the primary intracranial tumors[14]. According to the WHO 2007 classification system, the meningiomas are classified into 3 histological grades and 15 subtypes. This histopathological classification is generally used to predict the clinical course of meningioma. Most meningiomas are benign, well-circumscribed, slow growing tumors corresponding to WHO grade I[3] and usually follows uneventful clinical course. Some meningiomas, including WHO grade II (atypical) and grade III (anaplastic) tumors, are clinically and histologically aggressive. Grade II meningioma account for 4.7% to 7.2% and Grade III tumors comprises 1.0 to 2.8% of all the meningiomas[69]; however much larger proportion, 20% of the meningioma, demonstrates aggressive histological and/or clinical behavior[5]. This suggests that a borderline group of grade I meningioma also exists which behaves aggressively and might have recurrent or progressive disease[9]. Therefore, a histopathological grading alone might not accurately correlate with the patient outcome. It is important to distinguish WHO-grade I meningiomas with aggressive behavior from their non-aggressive counterparts. Several immunohistochemical parameters including Ki-67/ MIB-1, MMP-9, PR, ER are used as an adjunct to the histopathological grading to predict the meningioma prognosis.[4,1013] Similarly, several radiological features are used in conjunction with histopathological grading to identify benign versus aggressive meningioma features. The loss of tumor-brain interface, presence of PTE, irregular tumor shape, heterogeneous enhancement on MRI, decreased apparent diffusion coefficient (ADC) in diffusion weighted imaging (DWI) and fluorodeoxyglucose F [8]PET predicts the aggressive histological and clinical behavior of meningioma [2,10,1421,2224,25,15].Despite of the numerous studies determining the clinical, radiological and histological parameters associated with aggressive meningioma behavior; the accurate prediction of meningioma behavior is challenging. We set out to determine if the radiological parameters can predict histopathological aggressive meningioma, and based on that propose a classification to predict survival and aggressive meningioma behavior.

Material and Methods

After approval from the institutional review board, a retrospective review of the medical records, preoperative imaging and operative details was conducted for each patient. This retrospective study was approved by Nanfang Hospital Medical Ethics Committee. Patient records/information was anonymized and de-identified prior to analysis. The clinical records of participants in this study were de-identified prior to analysis.

Patient demographics

Between 2003 and 2006, 246 patients with intracranial convexity meningiomas underwent surgery as the primary treatment at our institution. Patients underwent surgical resection without preoperative embolization. To nullify the effect of location (skull base versus convexity) [8,9,23],extent of resection[26,27]and preoperative functional status of the patients, we only included patients with convexity meningioma, Karnofsky performance score (KPS) of ≥60 and in whom Simpson grade I resection was achieved. Preoperative MRI, operative notes and surgical specimen were re-evaluated. The histopathology slides were re-evaluated and the histopathological diagnosis was classified based on the 2007 WHO classification system for meningioma[3].

MR Imaging

MRI examinations were performed using a 1.5-T machine for patients operated on before 2004 and a 3-T machine for patients operated on after 2004 (General Electric Signa Excite HD). The MRI protocol included the following sequences: T1-weighted images (TR/TE, 436/21 msec), T2-weighted images (TR/TE, 5000/125 msec; echo train length 8), diffusion-weighted imaging (DWI, TR/TE,8000/65msec;Δ/δ37/32 msec) and FLAIR images (TR/TE/TI, 9000/145/2100 msec). Slice thickness was 5 mm, and the field of view varied between 18 and 30 cm. We also obtained axial, coronal, and sagittal T1-weighted images after administration of 0.1 mmol/kg of body weight of Gd-DTPA.

According to the WHO 2007 classification system, increased cellularity, necrosis and brain invasion are the histological features associated with non-Grade I tumor[3].Radiological appearance of these histological features has been described as hyperintensity on diffusion-weighted imaging (DWI), heterogeneous enhancement on T1-weighted gadolinium (Gd) enhanced MRI and cortical penetration or disappearance of arachnoid layer on T2-weighted MRI. Few studies have demonstrated that peritumoral edema (PTE) [2,8,15,28,29]and tumor shape [8,14,21,30] are associated with aggressive behavior or higher WHO grades. We evaluated the association of this five radiological parameters to the aggressive meningioma behavior; (1) signal intensity on DWI, (2) heterogeneity on T1-weighted Gd enhanced MRI, (3) Arachnoid layer on T2-weighted MRI, (4) PTE on T2-weighted MRI, (5) Tumor shape.

Scoring of radiologic characteristics

Radiological features were scored as: DWI signal intensity (hyperintense to grey matter = 1,others = 0); T1-weighted Gd-enhanced MRI (heterogeneity = 1, homogeneity = 0); arachnoid layer on T2-weighted MRI (disappeared or disintegrated = 1, intact = 0); PTE on T2-weighted MRI (tumor with edema = 1, tumor without edema = 0) and tumor shape (tumor with irregular shape, including mushroom shape or lobulated = 1, tumor with regular shape, including globular shape = 0) (Fig. 1). The lowest score was 0 and the highest was 5 (Table 1). Preoperative MRI was evaluated and total score was calculated for each patient. Based on their preoperative MRI scoring, all patients were classified into 3 groups: group one = 0–1, group two = 2–3 and group three 4–5. The survival time, progression free survival and overall survival (OS) rates of each group were analyzed. The survival outcome was evaluated as favorable if the patients were alive at the last follow-up and unfavorable if the status of patient was dead.

thumbnail
Fig 1. Radiologic characteristics of meningiomas in preoperative MRI.

a) T2-weighted MRI demonstrating the complete arachnoid ring (black arrow) and no peritumoral edema; b) T2-weighted MRI demonstrating the disappearance of arachnoid ring (black arrow head) and peritumoral edema c) T1-weighted gadolinium enhanced MRI demonstrating heterogeneous enhancement and irregular shape of the tumor; d) T1-weighted gadolinium enhanced MRI demonstrating homogenous enhancement and regular shape of tumor; e) Diffusion weighted MRI demonstrating hyperintense lesion; f) Diffusion weighted MRI demonstrating isointense lesion.

https://doi.org/10.1371/journal.pone.0118908.g001

thumbnail
Table 1. Summary of the preoperative radiological classification.

https://doi.org/10.1371/journal.pone.0118908.t001

Statistical analysis

The continuous variables were represented as mean ±SD. Univariate analyses were conducted to examine the association between radiological and histopathological features. Multivariate logistic regression model was used to evaluate if the radiological factors predict the occurrence of non-grade I meningioma (grade II and grade III). Kaplan-Meier analysis was utilized to evaluate the progression free survival and overall survival time and rate. Cox regression analysis model was used to determine the predictors of prognosis. The independent variable included; age at the time of surgery, gender, WHO-grading, preoperative radiological parameters and the radiological classification groups. Kappa test was used to study the accuracy of association between histopathological grading and proposed radiological scoring. For all analysis, p < 0.05 were considered statistically significant. SPSS 20 (Chicago, Inc, IBM) was used for all the statistical analysis.

Results

The mean age of 101 men and 145 women was 57.5 years (range, 7–80 years).

MRI findings and scoring

Thirty-three percent patients (82/246) demonstrated hyperintensity on DWI image, 32% (79/246) of patients showed heterogeneity on T1-weighted gadolinium enhanced MRI; 41% (100/246) had disruption of arachnoid on T2-weighted MRI, 39% (97/246) demonstrated PTE on T2-weighted MRI, and 24% (59/246) patients had irregular shape of tumor. Scoring for preoperative radiological features revealed, 17.4% (43/246) of the patients had preoperative radiological score of 0, 36% (88/246) had score 1, 21% (52/246) score 2, 14% (35/246) score 3, 8% (20/246) score 4, and 3% (8/246) had score 5. Based on this, 53% (131/246) of patients were classified as group I, 35% (87/246) group II and 11% (28/246) were group III.

Histopathological classification

Based on WHO 2007 histopathological classification system, 76% (187/246) of the patients were grade I, 15% (37/246) were grade II and 9% (22/246) were grade III. For data analysis, WHO grade II and III patients were defined as non-grade I tumors, 24% (59/246) of the patients. Seventy percent (131/187) of patients with grade I tumors were classified as group I, 30% (56/187) were group II, and 0% were group III. For non-grade I tumors 0% of patients were group I, 54.2% (32/59) were group II and 47.4% (28/59) were group III. There was a significant correlation between the radiological groups and histopathological grades of meningioma (Pearson Chi-square-136.2, P< 0.0001). Among Group II (87) cases, 64% (56/87) of patients were WHO grade-I and 36% (31/87) were non-grade I tumors.

All the five preoperative radiological scoring parameters were significantly associated with non-grade I tumor, controlling for age and gender (Table 2). Hyperintensity on DWI was the strongest independent predictor of non-grade I meningioma (P<0.001, OR-17, CI-5.8–47.6), followed by disruption of arachnoid layer (P<0.001, OR-14, CI-4.3–42.3), PTE on T2-weighted MRI (P<0.001, OR-9, CI-3.1–26.8), heterogenicitiy on T1-weighted gadolinium enhanced MRI (P<0.001, OR-6.1, CI-2.2–17.1) and irregular shape of the tumor (P<0.001, OR-6.1, CI-2.1–17.5) (Table 2). The preoperative radiological scoring system demonstrated moderate accuracy (Kappa value, 0.511; p<0.001).

thumbnail
Table 2. Logistic Regression analysis for the clinical and radiological parameters as predictors of WHO non-grade I meningioma.

https://doi.org/10.1371/journal.pone.0118908.t002

Overall survival

The mean follow-up period was 94.6 months (range 12–117 months; median 80.4 months). The median overall survival was 97.5 ± 2.2 months. The median survival in patients with WHO grade I tumors was 111.4 ± 1.5 months and 60.7 ±4.1 months in patients with WHO non-grade I tumors (Fig. 2). For patients in groups I, II and III, the median survival was 114.1±1.2, 88± 3.3, 43.2± 5.1 months respectively (Fig. 3).The overall survival rate was 78.5%; the patients in groups I, II and III had the survival rates of 96.2%, 72.4% and 14.3% respectively. Fig. 4 demonstrates the survival relationship among various groups and grades of meningioma. In cox regression analysis model, age at the time of surgery (P<0.0001, OR–1.039, CI—1.017–1.062), WHO-grade (P = 0.017, OR—3.014, CI—1.217–7.465), and preoperative radiological classification groups II (P = 0.002, OR—6.194, CI—1.956–19.610) and group III (P<0.0001, OR—21.658, CI—5.701–82.273) were independent predictors of unfavorable overall survival outcomes (Table 3).

thumbnail
Fig 2. Kaplan-Meier Curve for Survival analysis among the group I, II and III.

https://doi.org/10.1371/journal.pone.0118908.g002

thumbnail
Fig 3. Kaplan-Meier Curve for Survival analysis for WHO grade I and non-grade I tumors.

https://doi.org/10.1371/journal.pone.0118908.g003

thumbnail
Fig 4. Bar-graph demonstrating the survival time in group I, II and III in relation to WHO grades of meningioma.

https://doi.org/10.1371/journal.pone.0118908.g004

thumbnail
Table 3. Cox Regression analysis for the clinical and radiological parameters as predictors of unfavorable survival outcomes.

https://doi.org/10.1371/journal.pone.0118908.t003

Progression free survival-

Fig. 5 demonstrates PFS in groups I, II and III. The median PFS was 86.2 ± 2.7 months and the PFS rate was 67%. The median PFS for patients in groups I, II and III was 115.7± 0.8, 58.5±3.9, 18.2±1.7 months and the PFS rate was 98.5%, 42% and 0% respectively. The median PFS rate and time for patients with grade I tumors was 104.5±2.1 and 86% months and 27.5 ± 2.1 months and 10% for non-grade I tumors. The rate of recurrence was 100% in group III patients and 1.5% and 59% in groups-I and II respectively. In cox regression analyses, the WHO grade (P<0.001, OR–4.712, CI—2.574–8.627), group II (P<0.001, OR–52.504, CI–12.367–222.9) and group III (P<0.0001, OR–249.22, CI—51.822–1198.561) were predictors of recurrence (Table 4).

thumbnail
Fig 5. Progression free survival analysis among the groups I, II and III.

https://doi.org/10.1371/journal.pone.0118908.g005

thumbnail
Table 4. Cox Regression analysis for the clinical and radiological parameters as predictors of recurrence.

https://doi.org/10.1371/journal.pone.0118908.t004

Discussion

In present study, hyperintensity on DWI-MRI, disruption of arachnoid ring on T2-weighted MRI, PTE on T2-weighted MRI, heterogeneity on T1-weighted gadolinium enhanced MRI, and irregular shape of tumor were all significant predictor of WHO non-grade I meningioma. Among the five parameters, hyperintensity on DWI was the strongest independent predictor of non-grade I meningioma in our study. Several authors have demonstrated that hyperintensity on DWI and decreased co-efficient on ADC values predicts the higher histological grades of meningioma [16,24,25,34]. However, others have demonstrated that DWI cannot accurately predict the histopathological grading of meningioma [36,37]. Based on the hypothesis, that the diffusion of water to and from the cells is highly dependent on the ratio of intracellular and extracellular space, DWI is used to differentiate the tumor grades [3,21,5,31]. High grade meningiomas are characterized by increased tumor cellularity, increased nucleus/cytoplasmic ratio, small cell size, and increased mitotic cells, which restricts the water diffusion; depicting as hyperintensity on DWI [25,3235]. Another pathological feature associated with aggressive meningioma behavior is disruption of the brain-tumor interface [17,20,21].The presence of arachnoid ring on T2-weighted MRI indicates a clear brain-tumor interface[38]. The high-grade meningioma, can penetrate into the brain parenchyma by direct invasion of the tumor cells into the neurovascular tissue [39]. The slow growing tumor, however, penetrates the sub-pial tissue and causes absorption of arachnoid membrane resulting in disruption of the brain-tumor interface. Thus, disruption of the hyperintense arachnoid ring on the T2-weighted MRI represents not only histopathological aggressiveness but also the invasive nature of otherwise benign grade I meningiomas.

The signal intensity on T2-weighted MRI is associated with the amount of PTE and histological type of meningioma [10,15,28,29].Surgically, the presence of PTE might indicate a more difficult tumor resection, aggressive meningioma and disruption of arachnoid layer at the tumor brain-interface [23,40]. PTE on the MRI, however, has been attributed to several other factors including tumor size [40,41], location [17,42,43], histological grading [17,19,44], tumor vascularity [19,29,40,45], tumor-related venous obstruction [29,46],impairment of blood-brain barrier [2,20,23,29,46],presence of pial-cortical blood supply [29,47],vascular endothelial growth factor [45] and irregular tumor margin [29,41].Thus the presence of PTE alone might not accurately predict the aggressiveness of meningioma. Similarly, heterogeneous enhancement on MRI is another feature associated with high-grade meningioma. It indicates intra-tumoral necrosis and heterogeneous distribution of the proliferating cells [2,6,7]. Some WHO- grade I meningiomas with large size, calcification or cystic degeneration, however, also demonstrates heterogeneous enhancement on MRI. Thus, the sole presence of heterogeneous enhancement on MRI might not accurately predict the aggressive meningioma behavior. Traditionally, the irregular tumor shape, is associated with high-grade meningiomas. The WHO grade-I meningiomas usually are globular shape [2,8,29]; however, in instances where the vascular supply to the tumor is impeded, the part of tumor deprived of blood supply undergoes necrosis and dies. This can cause a lobulated appearance of the otherwise benign tumor. Thus, the shape of tumor alone might be a disguise for meningioma aggressiveness.

The proposed preoperative radiological classification predicted the aggressive histopathological, and survival outcomes in patients with convexity meningiomas. There was a moderately accurate association between the preoperative scoring system and WHO histopathological classification system. The patients with group II and III were more likely to have unfavorable survival outcome compared to patients with group I. Most WHO grade I patients were classified as group I and had favorable survival outcomes, however, 30% (56/187) of WHO-grade I tumors were classified as group II. The overall survival was significantly higher in patients with histopathological grade III and preoperative radiological classification group II meningioma compared to grade III and group III meningiomas. Similarly, the patients with grade I and group I had higher survival rates compared to grade I and group II. Therefore, there exist a group of grade I meningioma that have aggressive radiological features and clinical behavior; however, any of this five preoperative MRI parameters alone might not accurately predict the aggressive clinical behavior of all grade I and non-grade I meningiomas. The preoperative radiological classification, presented in the current study, can be used as a supplement to the WHO histopathological meningioma grading, to accurately predict the aggressive behavior of convexity meningioma.

Study limitations

Despite the contributions this study makes to the literature, there are several limitations to this study. First, the study suffers from the inherent bias introduced by its retrospective study design. Second, the study is underpowered due to relatively small number of patients in groups II and III. Further studies are needed to validate this classification, which will then be able to define the need for additional treatment including postoperative radiotherapy or chemotherapy and the need for shorter follow-up in patients with groups II and III convexity meningioma.

Conclusion

We introduce a preoperative radiologic classification to predict the radiological and clinical aggressive behavior of convexity meningioma. Group I meningioma demonstrated benign radiologic, histopathologic and clinical behavior; group III demonstrated aggressive radiologic, histopathologic and clinical behavior. Group II meningioma might be considered intermediate as some histopathologically benign tumors belonged to group II; the need for more aggressive follow-up and/or treatment should be further investigated.

Author Contributions

Conceived and designed the experiments: YL SC SQ. Analyzed the data: SC YL. Wrote the paper: YL SC. Acquisition of data: YL. Critically revised the article: SC YL JP SQ. Reviewed submitted version of manuscript: SC YL JP SJ MC. Approved the final version of the manuscript on behalf of all authors: YL. Statistical analysis: YL SC. Administrative/technical/material support: SJ MC JP. Study supervision: JP SQ.

Reference

  1. 1. Bondy M, Ligon BL. Epidemiology and etiology of intracranial meningiomas: a review. Journal of neuro-oncology. 1996;29(3):197–205. Epub 1996/09/01. pmid:8858525
  2. 2. Kawahara Y, Nakada M, Hayashi Y, Kai Y, Hayashi Y, Uchiyama N, et al. Prediction of high-grade meningioma by preoperative MRI assessment. Journal of neuro-oncology. 2012;108(1):147–52. Epub 2012/02/14. pmid:22327898
  3. 3. Perry A LD, Scheithauer BW, Budka H, von Diemling A. Meningiomas. In: Louis DN OH, Wiestler OD, editor. World Health Organization Classification of Tumours of the Central Nervous System. 4. ed. Lyon: IARC 2007. p. 164–72.
  4. 4. Riemenschneider MJ, Perry A, Reifenberger G. Histological classification and molecular genetics of meningiomas. Lancet neurology. 2006;5(12):1045–54. Epub 2006/11/18. pmid:17110285
  5. 5. Ayerbe J, Lobato RD, de la Cruz J, Alday R, Rivas JJ, Gomez PA, et al. Risk factors predicting recurrence in patients operated on for intracranial meningioma. A multivariate analysis. Acta Neurochir (Wien). 1999;141(9):921–32. Epub 1999/10/20. pmid:10526073
  6. 6. Durand A, Labrousse F, Jouvet A, Bauchet L, Kalamarides M, Menei P, et al. WHO grade II and III meningiomas: a study of prognostic factors. Journal of neuro-oncology. 2009;95(3):367–75. Epub 2009/06/30. pmid:19562258
  7. 7. Ildan F, Erman T, Gocer AI, Tuna M, Bagdatoglu H, Cetinalp E, et al. Predicting the probability of meningioma recurrence in the preoperative and early postoperative period: a multivariate analysis in the midterm follow-up. Skull base: official journal of North American Skull Base Society [et al]. 2007;17(3):157–71. Epub 2007/11/02. PubMed Central PMCID: PMCPMC1888737.
  8. 8. McGovern SL, Aldape KD, Munsell MF, Mahajan A, DeMonte F, Woo SY. A comparison of World Health Organization tumor grades at recurrence in patients with non-skull base and skull base meningiomas. Journal of neurosurgery. 2010;112(5):925–33. Epub 2009/10/06. pmid:19799498
  9. 9. Perry A, Stafford SL, Scheithauer BW, Suman VJ, Lohse CM. Meningioma grading: an analysis of histologic parameters. The American journal of surgical pathology. 1997;21(12):1455–65. Epub 1997/12/31. pmid:9414189
  10. 10. Ildan F, Tuna M, Gocer AP, Boyar B, Bagdatoglu H, Sen O, et al. Correlation of the relationships of brain-tumor interfaces, magnetic resonance imaging, and angiographic findings to predict cleavage of meningiomas. Journal of neurosurgery. 1999;91(3):384–90. Epub 1999/09/02. pmid:10470811
  11. 11. Jensen R, Lee J. Predicting outcomes of patients with intracranial meningiomas using molecular markers of hypoxia, vascularity, and proliferation. Neurosurgery. 2012;71(1):146–56. Epub 2012/04/05. pmid:22472549
  12. 12. Mawrin C, Perry A. Pathological classification and molecular genetics of meningiomas. Journal of neuro-oncology. 2010;99(3):379–91. Epub 2010/09/03. pmid:20809251
  13. 13. Vranic A, Popovic M, Cor A, Prestor B, Pizem J. Mitotic count, brain invasion, and location are independent predictors of recurrence-free survival in primary atypical and malignant meningiomas: a study of 86 patients. Neurosurgery. 2010;67(4):1124–32. Epub 2010/10/01. pmid:20881577
  14. 14. Hashiba T, Hashimoto N, Maruno M, Izumoto S, Suzuki T, Kagawa N, et al. Scoring radiologic characteristics to predict proliferative potential in meningiomas. Brain tumor pathology. 2006;23(1):49–54. Epub 2007/12/21. pmid:18095119
  15. 15. Mantle RE, Lach B, Delgado MR, Baeesa S, Belanger G. Predicting the probability of meningioma recurrence based on the quantity of peritumoral brain edema on computerized tomography scanning. Journal of neurosurgery. 1999;91(3):375–83. Epub 1999/09/02. pmid:10470810
  16. 16. Simis A, Pires de Aguiar PH, Leite CC, Santana PA Jr., Rosemberg S, Teixeira MJ. Peritumoral brain edema in benign meningiomas: correlation with clinical, radiologic, and surgical factors and possible role on recurrence. Surgical neurology. 2008;70(5):471–7; discussion 7. Epub 2008/07/01. pmid:18586307
  17. 17. Watanabe Y, Yamasaki F, Kajiwara Y, Takayasu T, Nosaka R, Akiyama Y, et al. Preoperative histological grading of meningiomas using apparent diffusion coefficient at 3T MRI. European journal of radiology. 2013;82(4):658–63. Epub 2013/01/15. pmid:23313707
  18. 18. Weber DC, Lovblad KO, Rogers L. New pathology classification, imagery techniques and prospective trials for meningiomas: the future looks bright. Current opinion in neurology. 2010;23(6):563–70. Epub 2010/10/05. pmid:20885321
  19. 19. Chen TC, Zee CS, Miller CA, Weiss MH, Tang G, Chin L, et al. Magnetic resonance imaging and pathological correlates of meningiomas. Neurosurgery. 1992;31(6):1015–21; discussion 21–2. Epub 1992/12/01. pmid:1281915
  20. 20. Nakasu S, Nakasu Y, Nakajima M, Matsuda M, Handa J. Preoperative identification of meningiomas that are highly likely to recur. Journal of neurosurgery. 1999;90(3):455–62. Epub 1999/03/06. pmid:10067913
  21. 21. Ohba S, Kobayashi M, Horiguchi T, Onozuka S, Yoshida K, Ohira T, et al. Long-term surgical outcome and biological prognostic factors in patients with skull base meningiomas. Journal of neurosurgery. 2011;114(5):1278–87. Epub 2010/12/21. pmid:21166572
  22. 22. Hasseleid BF, Meling TR, Ronning P, Scheie D, Helseth E. Surgery for convexity meningioma: Simpson Grade I resection as the goal: clinical article. Journal of neurosurgery. 2012;117(6):999–1006. Epub 2012/10/16. pmid:23061394
  23. 23. Qi ST, Liu Y, Pan J, Chotai S, Fang LX. A radiopathological classification of dural tail sign of meningiomas. Journal of neurosurgery. 2012;117(4):645–53. Epub 2012/07/31. pmid:22839654
  24. 24. Kaplan RD, Coons S, Drayer BP, Bird CR, Johnson PC. MR characteristics of meningioma subtypes at 1.5 tesla. Journal of computer assisted tomography. 1992;16(3):366–71. Epub 1992/05/01. pmid:1592917
  25. 25. Tamiya T, Ono Y, Matsumoto K, Ohmoto T. Peritumoral brain edema in intracranial meningiomas: effects of radiological and histological factors. Neurosurgery. 2001;49(5):1046–51; discussion 51–2. Epub 2002/02/16. pmid:11846896
  26. 26. Alvarez F, Roda JM, Perez Romero M, Morales C, Sarmiento MA, Blazquez MG. Malignant and atypical meningiomas: a reappraisal of clinical, histological, and computed tomographic features. Neurosurgery. 1987;20(5):688–94. Epub 1987/05/01. pmid:3601014
  27. 27. New PF, Hesselink JR, O’Carroll CP, Kleinman GM. Malignant meningiomas: CT and histologic criteria, including a new CT sign. AJNR American journal of neuroradiology. 1982;3(3):267–76. Epub 1982/05/01. pmid:6805276
  28. 28. Jaaskelainen J. Seemingly complete removal of histologically benign intracranial meningioma: late recurrence rate and factors predicting recurrence in 657 patients. A multivariate analysis. Surgical neurology. 1986;26(5):461–9. Epub 1986/11/01. pmid:3764651
  29. 29. Filippi CG, Edgar MA, Uluǧ AM, Prowda JC, Heier LA, Zimmerman RD. Appearance of Meningiomas on Diffusion-weighted Images: Correlating Diffusion Constants with Histopathologic Findings. American Journal of Neuroradiology. 2001;22(1):65–72. pmid:11158890
  30. 30. Hakyemez B, Yildirim N, Gokalp G, Erdogan C, Parlak M. The contribution of diffusion-weighted MR imaging to distinguishing typical from atypical meningiomas. Neuroradiology. 2006;48(8):513–20. Epub 2006/06/21. pmid:16786348
  31. 31. Santelli L, Ramondo G, Della Puppa A, Ermani M, Scienza R, d’Avella D, et al. Diffusion-weighted imaging does not predict histological grading in meningiomas. Acta Neurochir (Wien). 2010;152(8):1315–9; discussion 9. Epub 2010/04/30. pmid:20428902
  32. 32. Sanverdi SE, Ozgen B, Oguz KK, Mut M, Dolgun A, Soylemezoglu F, et al. Is diffusion-weighted imaging useful in grading and differentiating histopathological subtypes of meningiomas? European journal of radiology. 2012;81(9):2389–95. Epub 2011/07/05. pmid:21723681
  33. 33. Szafer A, Zhong J, Anderson AW, Gore JC. Diffusion-weighted imaging in tissues: theoretical models. NMR in biomedicine. 1995;8(7–8):289–96. Epub 1995/11/01. pmid:8732183
  34. 34. Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, et al. Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. Journal of Magnetic Resonance Imaging. 1999;9(1):53–60. pmid:10030650
  35. 35. Nakamura M, Roser F, Jacobs C, Vorkapic P, Samii M. Medial sphenoid wing meningiomas: clinical outcome and recurrence rate. Neurosurgery. 2006;58(4):626–39, discussion-39. Epub 2006/04/01. pmid:16575326
  36. 36. Holodny AI, Nusbaum AO, Festa S, Pronin IN, Lee HJ, Kalnin AJ. Correlation between the degree of contrast enhancement and the volume of peritumoral edema in meningiomas and malignant gliomas. Neuroradiology. 1999;41(11):820–5. Epub 1999/12/22. pmid:10602854
  37. 37. Bitzer M, Wockel L, Morgalla M, Keller C, Friese S, Heiss E, et al. Peritumoural brain oedema in intracranial meningiomas: influence of tumour size, location and histology. Acta Neurochir (Wien). 1997;139(12):1136–42. Epub 1997/01/01. pmid:9479419
  38. 38. Inamura T, Nishio S, Takeshita I, Fujiwara S, Fukui M. Peritumoral brain edema in meningiomas—influence of vascular supply on its development. Neurosurgery. 1992;31(2):179–85. Epub 1992/08/01. pmid:1513424
  39. 39. Bradac GB, Ferszt R, Bender A, Schorner W. Peritumoral edema in meningiomas. A radiological and histological study. Neuroradiology. 1986;28(4):304–12. Epub 1986/01/01. pmid:3762907
  40. 40. Lobato RD, Alday R, Gomez PA, Rivas JJ, Dominguez J, Cabrera A, et al. Brain oedema in patients with intracranial meningioma. Correlation between clinical, radiological, and histological factors and the presence and intensity of oedema. Acta Neurochir (Wien). 1996;138(5):485–93; discussion 93–4. Epub 1996/01/01. pmid:8800322
  41. 41. de Vries J, Wakhloo AK. Cerebral oedema associated with WHO-I, WHO-II, and WHO-III-meningiomas: correlation of clinical, computed tomographic, operative and histological findings. Acta Neurochir (Wien). 1993;125(1–4):34–40. Epub 1993/01/01.
  42. 42. Lee KJ, Joo WI, Rha HK, Park HK, Chough JK, Hong YK, et al. Peritumoral brain edema in meningiomas: correlations between magnetic resonance imaging, angiography, and pathology. Surgical neurology. 2008;69(4):350–5; discussion 5. Epub 2008/02/12. pmid:18262249
  43. 43. Yoshioka H, Hama S, Taniguchi E, Sugiyama K, Arita K, Kurisu K. Peritumoral brain edema associated with meningioma: influence of vascular endothelial growth factor expression and vascular blood supply. Cancer. 1999;85(4):936–44. Epub 1999/03/26. pmid:10091773
  44. 44. Hiyama H, Kubo O, Tajika Y, Tohyama T, Takakura K. Meningiomas associated with peritumoural venous stasis: three types on cerebral angiogram. Acta Neurochir (Wien). 1994;129(1–2):31–8. Epub 1994/01/01.
  45. 45. Nakasu S, Nakasu Y, Matsumura K, Matsuda M, Handa J. Interface between the meningioma and the brain on magnetic resonance imaging. Surgical neurology. 1990;33(2):105–16. Epub 1990/02/01. pmid:2305355
  46. 46. Bitzer M, Wockel L, Luft AR, Wakhloo AK, Petersen D, Opitz H, et al. The importance of pial blood supply to the development of peritumoral brain edema in meningiomas. Journal of neurosurgery. 1997;87(3):368–73. Epub 1997/09/01. pmid:9285600
  47. 47. Otsuka S, Tamiya T, Ono Y, Michiue H, Kurozumi K, Daido S, et al. The relationship between peritumoral brain edema and the expression of vascular endothelial growth factor and its receptors in intracranial meningiomas. Journal of neuro-oncology. 2004;70(3):349–57. Epub 2005/01/25. pmid:15662977