MRI of intracranial meningiomas: correlations with histology and physical consistency

Carpeggiani, P.; Crisi, Girolamo; Trévisan, C.

doi:10.1007/bf00588715

Cited by 65 publications

(41 citation statements)

References 6 publications

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“…Studies carried out in humans to determine whether or not the subtype or grade of meningioma can be predicted from MRI have yielded generally inconclusive results. Some studies have found no correlation with tumor classification, [41][42][43][44] whereas others reported that the signal intensity of T2W images directly correlated with the histologic subtype in the majority of cases. [28][29][30][31]43 Tumors with hyperintensity on both T1 and T2W images, extensive edema, and central necrosis have been reported to exhibit more aggressive behavior and a higher grade, as have tumors with irregularly defined borders.…”

Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Imaging and Histological Classification of Intracranial Meningiomas in 112 Dogs

Sturges¹,

Dickinson²,

Bollen³

et al. 2008

Veterinary Internal Medicne

152

272

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Background: Intracranial meningiomas are the most common primary brain tumors in dogs. Classification of meningiomas by tumor grade and subtype has not been reported, and the value of magnetic resonance imaging (MRI) characteristics for predicting tumor subtype and grade has not been investigated.Hypothesis: Canine intracranial meningiomas are a heterogenous group of tumors with differing histological subtypes and grades. Prediction of histopathological classification is possible based on MRI characteristics.Animals: One hundred and twelve dogs with a histological diagnosis of intracranial meningioma. Methods: Retrospective observational study.Results: Meningiomas were overrepresented in the Golden Retriever and Boxer breeds with no sex predilection. The incidence of specific tumor grades was 56% benign (Grade I), 43% atypical (Grade II), and 1% malignant (Grade III). Grade I histological subtypes included meningothelial (43%), transitional (40%), microcystic (8%), psammomatous (6%), and angiomatous (3%). No statistically significant (P o .05) associations were found among tumor subtype or grade and any of the MRI features studied.Conclusions and Clinical Importance: Meningiomas in dogs differ from their counterparts in humans mainly in their higher incidence of atypical (Grade II) tumors observed. MRI characteristics do not allow for prediction of meningioma subtype or grade, emphasizing the necessity of histopathology for antemortem diagnosis. The higher incidence of atypical tumors in dogs may contribute to the poorer therapeutic response in dogs with meningiomas as compared with the response in humans with meningiomas.

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Section: Discussionmentioning

confidence: 99%

Magnetic Resonance Imaging and Histological Classification of Intracranial Meningiomas in 112 Dogs

Sturges¹,

Dickinson²,

Bollen³

et al. 2008

Veterinary Internal Medicne

152

272

View full text Add to dashboard Cite

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“…2 Although typical extra-axial benign meningiomas are easily diagnosed, distinction from more malignant histologic grades by CT or conventional MR imaging is difficult. 3 Neuroimaging features, such as heterogeneous appearance, heterogeneous enhancement, marked perilesional edema, irregular cerebral surface, mushrooming on the outer edge of the lesion, and bone destruction, are not unique or reliable for diagnosing atypical/malignant meningiomas. [4][5][6][7][8] A diagnostic method that can differentiate between benign and atypical/ malignant meningiomas would, therefore, be desirable for surgical planning.…”

mentioning

confidence: 99%

Diffusion-Weighted MR Imaging: Diagnosing Atypical or Malignant Meningiomas and Detecting Tumor Dedifferentiation

Nagar¹,

Ye²,

Ng³

et al. 2008

AJNR Am J Neuroradiol

193

123

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“…Over the past 2 decades, numerous studies have attempted to predict the consistency of cranial meningiomas by using a variety of advanced MRI techniques, ranging from T2-weighted to diffusion-tensor imaging to MR elastography sequences, mainly with limited success. 1,2,6,7,15 A prior study by Chen et al, from our institution, did show a correlation between soft tumor consistency and hyperintensity on T2-weighted MRI, 2 as have some additional studies. 6,9,12,15,19 Two prior studies have demonstrated an association between hard tumors and the fibroblastic meningioma subtype.…”

Section: Discussionmentioning

confidence: 61%

“…6,9,12,15,19 Two prior studies have demonstrated an association between hard tumors and the fibroblastic meningioma subtype. 1,7 Other studies, however, have found no relationship between imaging findings and consistency 17 or between histology and consistency. 17 The greatest success, to date, in demonstrating a consistency-imaging relationship has been a study utilizing diffusion-tensor imaging by Kashimura et al, who in 2007 reported an improved ability to predict tumor consistency as "hard" or "soft" in 29 patients with meningiomas.…”

Section: Discussionmentioning

confidence: 98%

A proposed grading system for standardizing tumor consistency of intracranial meningiomas

Zada

Yashar

Robison

et al. 2013

FOC

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Object Tumor consistency plays an important and underrecognized role in the surgeon's ability to resect meningiomas, especially with evolving trends toward minimally invasive and keyhole surgical approaches. Aside from descriptors such as “hard” or “soft,” no objective criteria exist for grading, studying, and conveying the consistency of meningiomas. Methods The authors designed a practical 5-point scale for intraoperative grading of meningiomas based on the surgeon's ability to internally debulk the tumor and on the subsequent resistance to folding of the tumor capsule. Tumor consistency grades and features are as follows: 1) extremely soft tumor, internal debulking with suction only; 2) soft tumor, internal debulking mostly with suction, and remaining fibrous strands resected with easily folded capsule; 3) average consistency, tumor cannot be freely suctioned and requires mechanical debulking, and the capsule then folds with relative ease; 4) firm tumor, high degree of mechanical debulking required, and capsule remains difficult to fold; and 5) extremely firm, calcified tumor, approaches density of bone, and capsule does not fold. Additional grading categories included tumor heterogeneity (with minimum and maximum consistency scores) and a 3-point vascularity score. This grading system was prospectively assessed in 50 consecutive patients undergoing craniotomy for meningioma resection by 2 surgeons in an independent fashion. Grading scores were subjected to a linear weighted kappa analysis for interuser reliability. Results Fifty patients (100 scores) were included in the analysis. The mean maximal tumor diameter was 4.3 cm. The distribution of overall tumor consistency scores was as follows: Grade 1, 4%; Grade 2, 9%; Grade 3, 43%; Grade 4, 44%; and Grade 5, 0%. Regions of Grade 5 consistency were reported only focally in 14% of heterogeneous tumors. Tumors were designated as homogeneous in 68% and heterogeneous in 32% of grades. The kappa analysis score for overall tumor consistency grade was 0.87 (SE 0.06, 95% CI 0.76–0.99), with 90% user agreement. Kappa analysis scores for minimum and maximum grades of tumor regions were 0.69 (agreement 72%) and 0.75 (agreement 78%), respectively. The kappa analysis score for tumor vascularity grading was 0.56 (agreement 76%). Overall consistency did not correlate with patient age, tumor location, or tumor size. A higher tumor vascularity grade was associated with a larger tumor diameter (p = 0.045) and with skull base location (p = 0.02). Conclusions The proposed grading system provides a reliable, practical, and objective assessment of meningioma consistency and facilitates communication among providers. This system also accounts for heterogeneity in tumor consistency. With the proposed scale, meningioma consistency can be standardized as groundwork for future studies relating to surgical outcomes, predictability of consistency and vascularity using neuroimaging techniques, and effectiveness of various surgical instruments.

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MRI of intracranial meningiomas: correlations with histology and physical consistency

Cited by 65 publications

References 6 publications

Magnetic Resonance Imaging and Histological Classification of Intracranial Meningiomas in 112 Dogs

Magnetic Resonance Imaging and Histological Classification of Intracranial Meningiomas in 112 Dogs

Diffusion-Weighted MR Imaging: Diagnosing Atypical or Malignant Meningiomas and Detecting Tumor Dedifferentiation

A proposed grading system for standardizing tumor consistency of intracranial meningiomas

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