Department of Neurosurgery, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy.
Department of Neurological Surgery, University of Virginia Health Science Center, Charlottesville, Virginia.
Oper Neurosurg (Hagerstown). 2019 Aug 1;17(2):227-236. doi: 10.1093/ons/opy323.
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.
To describe the first large-scale implementation of SE in oncological neurosurgery for lesions discrimination and characterization.
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.
(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.
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.
超声弹性成像是一种能够评估组织力学特性的超声成像技术。应变成像（SE）是一种定性的超声弹性成像方式，具有广泛的临床应用，但迄今为止，其在脑肿瘤手术中的应用非常有限。
描述 SE 在肿瘤神经外科中用于病变鉴别和特征描述的首次大规模应用。
我们分析了 64 例患者的回顾性数据，旨在：（i）评估病变和周围脑的硬度，（ii）评估 B 模式和 SE 的相关性，（iii）对胶质瘤特征进行亚组分析。
（i）在所有情况下，我们都使用 SE 可视化了病变和周围的脑，允许进行定性的硬度评估。（ii）在 90%的病例中，B 模式和 SE 中的病变表现具有相同的形态和边界，可完全匹配。在 64%的病例中，SE 中的病变边界比 B 模式更清晰。（iii）在 76%的病例中，SE 中的胶质瘤边界比 B 模式更清晰。SE 和 B 模式中的病变形态/尺寸可完全匹配，占 89%。低级别（LGG）和高级别（HGG）胶质瘤在硬度和肿瘤与脑之间的硬度对比方面存在显著差异，LGG 比脑更硬，而 HGG 比脑更软（均 P < ·001）。SE 评分 2.5 作为阈值，在区分 LGG 和 HGG 方面具有 85.7%的敏感性和 94.7%的特异性。
SE 可用于了解检查中脑和病变的力学特性，并与 B 模式相比，更好地区分不同的组织。此外，SE 还可以区分 LGG 和 HGG。
