Introduction: Sonoelastography is an ultrasound technique capable of describing tissue stiffness through the assessment of the local axial strain distribution caused by an externally applied force. Strain elastography (SE) provides qualitative imaging of the tissues under exam and it has a wide range of clinical applications, but its use, so far, in brain tumor surgery is very limited.
In this work we describe the first large-scale implementation of SE in oncological neurosurgery for lesions discrimination and characterization.
Methods: We analysed 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 imaging, and (iii) performing subgroup analysis for gliomas characterization
Results: i) In all cases we visualized the lesion and the surrounding brain parenchyma 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%. In all low grade glioma (LGG) except 1, the tumor was found to be stiffer than brain while in all high grade glioma (HGG) except 3 cases, the tumor was found to be softer than brain. LGG and HGG were significantly different in terms of lesion stiffness, stiffness of the surrounding brain, and stiffness contrast between the lesion and the surrounding brain (all P<0·001).
Conclusions: SE allows to understand mechanical properties of the brain and lesions in exam and permits a better discrimination between different tissues compared to B-mode. Additionally, SE can differentiate between LGG and HGG.
Patient Care: In our experience SE has proven to be a safe, and a highly informative intra-operative real-time imaging technique capable of providing surgeon with morphological and physical information.
Learning Objectives: SE can be mastered by neurosurgeons and consistently and profitably applied to surgical procedures for all kind of brain lesions.
SE helps to better visualize a large variety of neurosurgical lesions and to assess their boundaries.
SE patterns correlate with brain tumor histopathology