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14.04.2021 | Magnetic Resonance

A new method for quantification and 3D visualization of brain tumor adhesion using slip interface imaging in patients with meningiomas

verfasst von: Ziying Yin, Xin Lu, Salomon Cohen Cohen, Yi Sui, Armando Manduca, Jamie J. Van Gompel, Richard L. Ehman, John Huston III

Erschienen in: European Radiology | Ausgabe 8/2021

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Abstract

Objectives

To develop an objective quantitative method to characterize and visualize meningioma-brain adhesion using MR elastography (MRE)-based slip interface imaging (SII).

Methods

This retrospective study included 47 meningiomas (training dataset: n = 35; testing dataset: n = 12) with MRE/SII examinations. Normalized octahedral shear strain (NOSS) values were calculated from the acquired MRE displacement data. The change in NOSS at the tumor boundary (ΔNOSS_bdy) was computed, from which a 3D ΔNOSS_bdy map of the tumor surface was created and the probability distribution of ΔNOSS_bdy over the entire tumor surface was calculated. Statistical features were calculated from the probability histogram. After eliminating highly correlated features, the capability of the remaining feature for tumor adhesion classification was assessed using a one-way ANOVA and ROC analysis.

Results

The magnitude and location of the tumor adhesion can be visualized by the reconstructed 3D ΔNOSS_bdy surface map. The entropy of the ΔNOSS_bdy histogram was significantly different between adherent tumors and partially/completely non-adherent tumors in both the training (AUC: 0.971) and testing datasets (AUC: 0.900). Based on the cutoff values obtained from the training set, the ΔNOSS_bdy entropy in the testing dataset yielded an accuracy of 0.83 for distinguishing adherent versus partially/non-adherent tumors, and 0.67 for distinguishing non-adherent versus completely/partially adherent tumors.

Conclusions

SII-derived ΔNOSS_bdy values are useful for quantification and classification of meningioma-brain adhesion. The reconstructed 3D ΔNOSS_bdy surface map presents the state and location of tumor adhesion in a “clinician-friendly” manner, and can identify meningiomas with a high risk of adhesion to adjacent brain parenchyma.

Key points

• MR elastography (MRE)–based slip interface imaging shows promise as an objective tool to preoperatively discriminate meningiomas with a high risk of intraoperative adhesion.

• Measurement of the change of shear strain at meningioma boundaries can provide quantitative metrics depicting the state of adhesion at the tumor-brain interface.

• The surface map of tumor adhesion shows promise in assisting precise adhesion localization, using a comprehensible, “clinician-friendly” 3D visualization.

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Sindou MP, Moussa A (1998) Most intracranial meningiomas are not cleavable tumors: anatomic-surgical evidence and angiographic predictibility. Neurosurgery 42:476–480CrossRef

Little KM, Friedman AH, Sampson JH, Wanibuchi M, Fukushima T (2005) Surgical management of petroclival meningiomas: defining resection goals based on risk of neurological morbidity and tumor recurrence rates in 137 patients. Neurosurgery 56:546–559CrossRef

Ouyang T, Zhang N, Wang L, Li Z, Chen J (2015) Sphenoid wing meningiomas: surgical strategies and evaluation of prognostic factors influencing clinical outcomes. Clin Neurol Neurosurg 134:85–90CrossRef

Ottenhausen M, Rumalla K, Younus I, Minkowitz S, Tsiouris AJ, Schwartz TH (2018) Predictors of postoperative motor function in rolandic meningiomas. J Neurosurg. https://doi.org/10.3171/2017.12.JNS172423:1-6

Takeguchi T, Miki H, Shimizu T et al (2003) Evaluation of the tumor-brain interface of intracranial meningiomas on MR imaging including FLAIR images. Magn Reson Med Sci 2:165–169CrossRef

Thenier-Villa JL, Alejandro Galárraga Campoverde R, Ramón De La Lama Zaragoza A, Conde Alonso C (2017) Predictors of morbidity and cleavage plane in surgical resection of pure convexity meningiomas using cerebrospinal fluid sensitive image subtraction magnetic resonance imaging. Neurol Med Chir (Tokyo) 57:35–43CrossRef

Alvernia JE, Sindou MP (2004) Preoperative neuroimaging findings as a predictor of the surgical plane of cleavage: prospective study of 100 consecutive cases of intracranial meningioma. J Neurosurg 100:422–430CrossRef

İldan F, Tuna M, Göcer AP et al (1999) Correlation of the relationships of brain—tumor interfaces, magnetic resonance imaging, and angiographic findings to predict cleavage of meningiomas. J Neurosurg 91:384–390CrossRef

Enokizono M, Morikawa M, Matsuo T et al (2014) The rim pattern of meningioma on 3D FLAIR imaging: correlation with tumor-brain adhesion and histological grading. Magn Reson Med Sci 13:251–260CrossRef

10.

Yin Z, Glaser KJ, Manduca A et al (2015) Slip interface imaging predicts tumor-brain adhesion in vestibular schwannomas. Radiology 277:507–517CrossRef

11.

Yin Z, Hughes JD, Trzasko JD et al (2017) Slip interface imaging based on MR-elastography preoperatively predicts meningioma–brain adhesion. J Magn Reson Imaging 46:1007–1016CrossRef

12.

Yamada S, Taoka T, Nakagawa I et al (2015) A magnetic resonance imaging technique to evaluate tumor–brain adhesion in meningioma: brain-surface motion imaging. World Neurosurg 83:102–107CrossRef

13.

Yin Z, Romano AJ, Manduca A, Ehman RL, Huston JI (2018) Stiffness and beyond: what MR elastography can tell us about brain structure and function under physiologic and pathologic conditions. Top Magn Reson Imaging 27:305–318CrossRef

14.

Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol 131:803–820CrossRef

15.

Tan ET, Lee SK, Weavers PT et al (2016) High slew-rate head-only gradient for improving distortion in echo planar imaging: preliminary experience. J Magn Reson Imaging 44:653–664CrossRef

16.

Foo TKF, Laskaris E, Vermilyea M et al (2018) Lightweight, compact, and high-performance 3T MR system for imaging the brain and extremities. Magn Reson Med 80:2232–2245CrossRef

17.

Vemuri P, Gunter JL, Senjem ML et al (2008) Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage 39:1186–1197CrossRef

18.

Bioucas-Dias JM, Valadao G (2007) Phase unwrapping via graph cuts. IEEE Trans Image Process 16:698–709CrossRef

19.

Kolmogorov V, Zabih R (2004) What energy functions can be minimized via graph cuts? IEEE Trans Pattern Anal Mach Intell 26:147–159CrossRef

20.

McGarry MD, Van Houten EE, Perrinez PR, Pattison AJ, Weaver JB, Paulsen KD (2011) An octahedral shear strain-based measure of SNR for 3D MR elastography. Phys Med Biol 56:N153–N164CrossRef

21.

Gloschat C, Aras K, Gupta S et al (2018) RHYTHM: an open source imaging toolkit for cardiac panoramic optical mapping. Sci Rep 8:2921CrossRef

22.

Sui Y, In M, Yin Z, Bernstein MA, Ehman RL, Huston JI (2020) High-resolution distortion-free whole-brain MR elastography using multiband DIADEM (DIADEM-MRE). 2020 International Society for Magnetic Resonance in Medicine Virtual Conference & Exhibition No 185

Titel: A new method for quantification and 3D visualization of brain tumor adhesion using slip interface imaging in patients with meningiomas
verfasst von: Ziying Yin
Xin Lu
Salomon Cohen Cohen
Yi Sui
Armando Manduca
Jamie J. Van Gompel
Richard L. Ehman
John Huston III
Publikationsdatum: 14.04.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 8/2021
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-07918-6

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A new method for quantification and 3D visualization of brain tumor adhesion using slip interface imaging in patients with meningiomas