Cepeda Santiago, García-García Sergio, Arrese Ignacio, Velasco-Casares María, Sarabia Rosario
Department of Neurosurgery, University Hospital Río Hortega, Calle Dulzaina, 2, 47012, Valladolid, Spain.
Department of Radiology, University Hospital Río Hortega, Calle Dulzaina, 2, 47012, Valladolid, Spain.
J Ultrasound. 2022 Mar;25(1):121-128. doi: 10.1007/s40477-021-00569-9. Epub 2021 Feb 16.
Predicting the survival of patients diagnosed with glioblastoma (GBM) is essential to guide surgical strategy and subsequent adjuvant therapies. Intraoperative ultrasound (IOUS) can contain biological information that could be correlated with overall survival (OS). We propose a simple extraction method and radiomic feature analysis based on IOUS imaging to estimate OS in GBM patients.
A retrospective study of surgically treated glioblastomas between March 2018 and November 2019 was performed. Patients with IOUS B-mode and strain elastography were included. After preprocessing, segmentation and extraction of radiomic features were performed with LIFEx software. An evaluation of semantic segmentation was carried out using the Dice similarity coefficient (DSC). Using univariate correlations, radiomic features associated with OS were selected. Subsequently, survival analysis was conducted using Cox univariate regression and Kaplan-Meier curves.
Sixteen patients were available for analysis. The DSC revealed excellent agreement for the segmentation of the tumour region. Of the 52 radiomic features, two texture features from B-mode (conventional mean and the grey-level zone length matrix/short-zone low grey-level emphasis [GLZLM_SZLGE]) and one texture feature from strain elastography (grey-level zone length matrix/long-zone high grey-level emphasis [GLZLM_LZHGE]) were significantly associated with OS. After establishing a cut-off point of the statistically significant radiomic features, we allocated patients in high- and low-risk groups. Kaplan-Meier curves revealed significant differences in OS.
IOUS-based quantitative texture analysis in glioblastomas is feasible. Radiomic tumour region characteristics in B-mode and elastography appear to be significantly associated with OS.
预测胶质母细胞瘤（GBM）患者的生存率对于指导手术策略及后续辅助治疗至关重要。术中超声（IOUS）可能包含与总生存期（OS）相关的生物学信息。我们提出一种基于IOUS成像的简单提取方法和影像组学特征分析，以评估GBM患者的OS。
对2018年3月至2019年11月接受手术治疗的胶质母细胞瘤患者进行回顾性研究。纳入具有IOUS B模式和应变弹性成像的患者。经过预处理后，使用LIFEx软件进行影像组学特征的分割和提取。使用Dice相似系数（DSC）对语义分割进行评估。通过单变量相关性分析，选择与OS相关的影像组学特征。随后，使用Cox单变量回归和Kaplan-Meier曲线进行生存分析。
16例患者可供分析。DSC显示肿瘤区域分割具有良好的一致性。在52个影像组学特征中，B模式的两个纹理特征（传统均值和灰度级区域长度矩阵/短区域低灰度级强调[GLZLM_SZLGE]）和应变弹性成像的一个纹理特征（灰度级区域长度矩阵/长区域高灰度级强调[GLZLM_LZHGE]）与OS显著相关。在确定具有统计学意义的影像组学特征的截断点后，我们将患者分为高风险和低风险组。Kaplan-Meier曲线显示OS存在显著差异。
基于IOUS的胶质母细胞瘤定量纹理分析是可行的。B模式和弹性成像中的影像组学肿瘤区域特征似乎与OS显著相关。
