151. Relationship between the overall survival in glioblastomas and the radiomic features of intraoperative ultrasound: a feasibility study
- Author
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Rosario Sarabia, María Velasco-Casares, Ignacio Arrese, Sergio García-García, and Santiago Cepeda
- Subjects
medicine.medical_specialty ,Technical Reports ,Brain tumor ,030218 nuclear medicine & medical imaging ,Intraoperative ultrasound ,03 medical and health sciences ,0302 clinical medicine ,Internal Medicine ,medicine ,Overall survival ,Humans ,Radiology, Nuclear Medicine and imaging ,Survival analysis ,Retrospective Studies ,Ultrasonography ,medicine.diagnostic_test ,business.industry ,Ultrasound ,Univariate ,General Medicine ,medicine.disease ,Elasticity Imaging Techniques ,Feasibility Studies ,Elastography ,Radiology ,business ,Glioblastoma ,030217 neurology & neurosurgery - Abstract
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40477-021-00569-9.
- Published
- 2020