Intraoperative Strain Elastosonography in Brain Tumor Surgery

Francesco Prada(Johns Hopkins University), Massimiliano Del Bene(Johns Hopkins University), Angela Dele Rampini(Johns Hopkins University), Luca Mattei(Johns Hopkins University), Cecilia Casali(Johns Hopkins University), Ignazio G. Vetrano(Johns Hopkins University), Antonio Giulio Gennari(Johns Hopkins University), Silvana Sdao(Johns Hopkins University), Marco Saini(Johns Hopkins University), Luca Maria Sconfienza(Johns Hopkins University), Francesco DiMeco(Johns Hopkins University)
Operative Neurosurgery
November 28, 2018
Cited by 64

Abstract

BACKGROUND: Sonoelastography is an ultrasound imaging technique able to assess mechanical properties of tissues. Strain elastography (SE) is a qualitative sonoelastographic modality with a wide range of clinical applications, but its use in brain tumor surgery has been so far very limited. OBJECTIVE: To describe the first large-scale implementation of SE in oncological neurosurgery for lesions discrimination and characterization. METHODS: We analyzed retrospective data from 64 patients aiming at (i) evaluating the stiffness of the lesion and of the surrounding brain, (ii) assessing the correspondence between B-mode and SE, and (iii) performing subgroup analysis for gliomas characterization. RESULTS: (i) In all cases, we visualized the lesion and the surrounding brain with SE, permitting a qualitative stiffness assessment. (ii) In 90% of cases, lesion representations in B-mode and SE were superimposable with identical morphology and margins. In 64% of cases, lesion margins were sharper in SE than in B-mode. (iii) In 76% of cases, glioma margins were sharper in SE than in B-mode. Lesions morphology/dimensions in SE and in B-mode were superimposable in 89%. Low-grade (LGG) and high-grade (HGG) gliomas were significantly different in terms of stiffness and stiffness contrast between tumors and brain, LGG appearing stiffer while HGG softer than brain (all P < ·001). A threshold of 2.5 SE score had 85.7% sensitivity and 94.7% specificity in differentiating LGG from HGG. CONCLUSION: SE allows to understand mechanical properties of the brain and lesions in examination and permits a better discrimination between different tissues compared to B-mode. Additionally, SE can differentiate between LGG and HGG.


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