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Clinical Neuroradiology
Published in: Clinical Neuroradiology 2/2017

01-06-2017 | Original Article

Rapid and Accurate MRI Segmentation of Peritumoral Brain Edema in Meningiomas

Authors: F. Latini, E.-M. Larsson, M. Ryttlefors

Published in: Clinical Neuroradiology | Issue 2/2017

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Abstract

Purpose

The extent of peritumoral brain edema (PTBE) in meningiomas commonly affects the clinical outcome. Despite its importance, edema volume is usually highly inaccurately approximated to a spheroid shape. We tested the accuracy and the reproducibility of semiautomatic lesion management software for the analysis of PTBE in a homogeneous case series of surgically confirmed intracranial meningiomas.

Methods

PTBE volume was calculated on magnetic resonance images in 50 patients with intracranial meningiomas using commercial lesion management software (Vue PACS Livewire, Carestream, Rochester, NY, USA). Inter and intraobserver agreement evaluation and a comparison between manual volume calculation, the semiautomatic software and spheroid approximation were performed in 22 randomly selected patients.

Results

The calculation of edema volume was possible in all cases irrespective of the extent of the signal changes. The median time for each calculation was 3 min. Interobserver and intraobserver agreement confirmed the reproducibility of the method. Comparison with standard (fully manual) calculation confirmed the accuracy of this software.

Conclusions

Our study showed a high level of reproducibility of this semiautomatic computational method for peritumoral brain edema. It is rapid and easy to use after relatively short training and is suitable for implementation in clinical practice.
Literature
1.
Go KG, Wilmink JT, Molenaar WM. Peritumoral brain edema associated with meningiomas. Neurosurgery. 1988;23:175–9.CrossRefPubMed
2.
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. Surg Neurol. 2008;70:471–7 (discussion 477).CrossRefPubMed
3.
Lieu AS, Howng SL. Intracranial meningiomas and epilepsy: incidence, prognosis and influencing factors. Epilepsy Res. 2000;38:45–52.CrossRefPubMed
4.
Lobato RD, Alday R, Gomez PA, Rivas JJ, Dominguez J, Cabrera A, Madero S, Ayerbe J. 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;138:485–93 (discussion 493–484).CrossRef
5.
Alaywan M, Sindou M. Prognostic factors in the surgery for intracranial meningioma. Role of the tumoral size and arterial vascularization originating from the pia mater. Study of 150 cases. Neurochirurgie. 1993;39:337–47.PubMed
6.
Arienta C, Caroli M, Crotti F, Villani R. Treatment of intracranial meningiomas in patients over 70 years old. Acta Neurochir (Wien). 1990;107:47–55.CrossRef
7.
Ildan F, Tuna M, Gocer AP, Boyar B, Bagdatoglu H, Sen O, Haciyakupoģlu S, Burgut HR. Correlation of the relationships of brain-tumor interfaces, magnetic resonance imaging, and angiographic findings to predict cleavage of meningiomas. J Neurosurg. 1999;91:384–90.CrossRefPubMed
8.
Vignes JR, Sesay M, Rezajooi K, Gimbert E, Liguoro D. Peritumoral edema and prognosis in intracranial meningioma surgery. J Clin Neurosci. 2008;15:764–8.CrossRefPubMed
9.
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. J Neurosurg. 1999;91:375–83.CrossRefPubMed
10.
Bitzer M, Wockel L, Morgalla M, Keller C, Friese S, Heiss E, Meyermann R, Grote E, Voigt K. Peritumoural brain oedema in intracranial meningiomas: influence of tumour size, location and histology. Acta Neurochir (Wien). 1997;139:1136–42.CrossRef
11.
Ding YS, Wang HD, Tang K, Hu ZG, Jin W, Yan W. Expression of vascular endothelial growth factor in human meningiomas and peritumoral brain areas. Ann Clin Lab Sci. 2008;38:344–51.PubMed
12.
Inamura T, Nishio S, Takeshita I, Fujiwara S, Fukui M. Peritumoral brain edema in meningiomas–influence of vascular supply on its development. Neurosurgery. 1992;31:179–85.CrossRefPubMed
13.
Nassehi D, Dyrbye H, Andresen M, Thomsen C, Juhler M, Laursen H, Broholm H. Vascular endothelial growth factor A protein level and gene expression in intracranial meningiomas with brain edema. APMIS. 2011;119:831–43.CrossRefPubMed
14.
Osawa T, Tosaka M, Nagaishi M, Yoshimoto Y. Factors affecting peritumoral brain edema in meningioma: special histological subtypes with prominently extensive edema. J Neurooncol. 2013;111:49–57.CrossRefPubMed
15.
Oya S, Kim SH, Sade B, Lee JH. The natural history of intracranial meningiomas. J Neurosurg. 2011;114:1250–6.CrossRefPubMed
16.
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;15, 85(4):936–44.CrossRef
17.
Lee KJ, Joo WI, Rha HK, Park HK, Chough JK, Hong YK, Park CK. Peritumoral brain edema in meningiomas: correlations between magnetic resonance imaging, angiography, and pathology. Surg Neurol. 2008;69(4):350–5.CrossRefPubMed
18.
Gurkanlar D, Er U, Sanli M, Ozkan M, Sekerci Z. Peritumoral brain edema in intracranial meningiomas. J Clin Neurosci. 2005;12(7):750–3.CrossRefPubMed
19.
Kim BW, Kim MS, Kim SW, Chang CH, Kim OL. Peritumoral brain edema in meningiomas: correlation of radiologic and pathologic features. J Korean Neurosurg Soc. 2011;49(1):26–30.CrossRefPubMedPubMedCentral
20.
Nassehi D, Sørensen LP, Dyrbye H, Thomsen C, Juhler M, Laursen H, Broholm H. Peritumoral brain edema in angiomatous supratentorial meningiomas: an investigation of the vascular endothelial growth factor A pathway. APMIS. 2013;121(11):1025–36.CrossRefPubMed
21.
Nakano T, Asano K, Miura H, Itoh S, Suzuki S. Meningiomas with brain edema: radiological characteristics on MRI and review of the literature. Clin Imaging. 2002;26(4):243–9.CrossRefPubMed
22.
Buhl R, Hugo HH, Mehdorn HM. Brain oedema in secretory meningiomas. J Clin Neurosci. 2001;8(Suppl 1):19–21.CrossRefPubMed
23.
Paek SH, Kim CY, Kim YY, Park IA, Kim MS, Kim DG, Jung HW. Correlation of clinical and biological parameters with peritumoral edema in meningioma. J Neurooncol. 2002;60:235–45.CrossRefPubMed
24.
Xie K, Yang J, Zhang ZG, Zhub YM. Semi-automated brain tumor and edema segmentation using MRI. Eur J Radiol. 2005;56:12–9.CrossRefPubMed
25.
Duong DH, Rostomily RC, Haynor DR, Keles GE, Berger MS. Measurement of tumor resection volumes from computerized images. Technical note. J Neurosurg. 1992;77(1):151–4.CrossRefPubMed
26.
Sanai N, Polley MY, Berger MS. Insular glioma resection: assessment of patient morbidity, survival, and tumor progression. J Neurosurg. 2010;112(1):1–9.CrossRefPubMed
27.
Shi WM, Wildrick DM, Sawaya R. Volumetric measurement of brain tumors from MR imaging. J Neurooncol. 1998;37(1):87–93.CrossRefPubMed
28.
Mazzara G, Velthuizen R, Pearlman J, Greenberg H, Wagner H. Brain tumor target determination for radiation treatment planning through automated MRI segmentation. Int J Radiat Oncol Biol Phys. 2004;59(1):300–12.CrossRefPubMed
29.
Gordillo N, Montseny E, Sobrevilla P. State of the art survey on MRI brain tumor segmentation. Magn Reson Imaging. 2013;31(8):1426–38.CrossRefPubMed
30.
Prastawa M, Bullitt E, Ho S, Gerig G. A brain tumor segmentation framework based on outlier detection. Med Image Anal. 2004;8(3):275–83.CrossRefPubMed
31.
Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS. Automatic tumor-segmentation using knowledge-based techniques. IEEE Trans Med Imaging. 1998;117:187–201.CrossRef
32.
Fletcher-Heath LM, Hall LO, Goldgof DB, Murtagh FR. Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artif Intell Med. 2001;21:43–63.CrossRefPubMed
33.
Saha BN, Ray N, Greiner R, Murtha A, Zhang H. Quick detection of brain tumors and edemas: a bounding box method using symmetry. Comput Med Imaging Graph. 2012;36(2):95–107.CrossRefPubMed
34.
Khotanlou H, Colliot O, Atifc J, Blocha I. 3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models. Fuzzy Sets Systems. 2009;160:1457–73.CrossRef
35.
Kaus MR, Warfield SK, Nabavi A, Chatzidakis E, Black PM, Jolesz FA, Kikinis R. Segmentation of meningiomas and low grade gliomas in MRI. In: Taylor C, Colchester A, editors. Lecture notes in computer science, MICCAI. Vol. 1679. Berlin: Springer; 1999. pp. 1–10.
36.
Clarke LP, Velthuizen RP, Camacho MA, Heine JJ, Vaidyanathan M, Hall LO, Thatcher RW, Silbiger ML. MRI segmentation: methods and applications. Magn Reson Imaging. 1995;13(3):343–68. Review.CrossRefPubMed
37.
Velthuizen RP, Clarke LP, Phuphanich S, Hall LO, Bensaid AM, Arrington JA, Greenberg HM, Silbiger ML. Unsupervised measurement of brain tumor volume on MR images. J Magn Reson Imaging. 1995;5(5):594–605.CrossRefPubMed
38.
Mortensen EN, Barrett WA. Interactive segmentation with intelligent scissors. Graph Models Image Process. 1998;60(5):349–84.CrossRef
39.
Zewei Z, Tianyue W, Li G, Tingting W, Lu X. An interactive method based on the Live Wire for segmentation of the breast in mammography images. Comput Math Methods Med. 2014:95:41–8.
40.
Folio LR, Sandouk A, Huang J, Solomon JM, Apolo AB. Consistency and efficiency of CT analysis of metastatic disease: semiautomated lesion management application within a PACS. AJR Am J Roentgenol. 2013;201(3):618–25.CrossRefPubMed
41.
Brenner DJ. Dose, volume and tumor control predictions in radiotherapy. Int J Radiat Oncol Biol Phys. 1993;26:171–9.CrossRefPubMed
42.
Haralick RM, Shapiro LG. Computer and robot vision. Vol. I. New York: Addison-Wesley; 1992. pp. 20–3.
Metadata
Title
Rapid and Accurate MRI Segmentation of Peritumoral Brain Edema in Meningiomas
Authors
F. Latini
E.-M. Larsson
M. Ryttlefors
Publication date
01-06-2017
Publisher
Springer Berlin Heidelberg
Published in
Clinical Neuroradiology / Issue 2/2017
Print ISSN: 1869-1439
Electronic ISSN: 1869-1447
DOI
https://doi.org/10.1007/s00062-015-0481-0

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