Abstract
From May 2015 to June 2016, data on 296 patients undergoing 1.5-Tesla MRI for symptoms of acute ischemic stroke were retrospectively collected. Conventional, echo-planar imaging (EPI) and echo train length (ETL)-T2-FLAIR were simultaneously obtained in 118 patients (first group), and conventional, ETL-, and repetition time (TR)-T2-FLAIR were simultaneously obtained in 178 patients (second group). A total of 595 radiomics features were extracted from one region-of-interest (ROI) reflecting the acute and chronic ischemic hyperintensity, and concordance correlation coefficients (CCC) of the radiomics features were calculated between the fast scanned and conventional T2-FLAIR for paired patients (1st group and 2nd group). Stabilities of the radiomics features were compared with the proportions of features with a CCC higher than 0.85, which were considered to be stable in the fast scanned T2-FLAIR. EPI-T2-FLAIR showed higher proportions of stable features than ETL-T2-FLAIR, and TR-T2-FLAIR also showed higher proportions of stable features than ETL-T2-FLAIR, both in acute and chronic ischemic hyperintensities of whole- and intersection masks (p < .002). Radiomics features in fast scanned T2-FLAIR showed variable stabilities according to the sequences compared with conventional T2-FLAIR. Therefore, radiomics features may be used cautiously in applications for feature analysis as their stability and robustness can be variable.
Highlights
The paradigm is shifting from qualitative visual assessment of medical imaging to quantitative data analysis with the development of high-throughput mining of low- to high dimensional data
This study showed a consistent tendency of higher proportions of reliable features in echo-planar imaging (EPI)-T2-Fluid attenuated inversion recovery (FLAIR) and TRT2-FLAIR than echo train length (ETL)-T2-FLAIR in both acute and chronic ischemic hyperintensities and for both whole- and intersection-ROI mask
Various image acquisitions of T2-FLAIR resulted in unstable radiomic features, which may lead to different radiomic features’ outcomes, such as prediction modeling
Summary
The paradigm is shifting from qualitative visual assessment of medical imaging to quantitative data analysis with the development of high-throughput mining of low- to high dimensional data. Among the various types of medical imaging, magnetic resonance imaging (MRI) has a variety of imaging acquisition methods and combinations of complicated parameters even in the same imaging sequences, which makes it difficult to apply radiomic features to MRI. Fast scanned techniques are essential in the acquisition of MRI because of the major limitation of MRI, the need for a long scan time, in emergency situations such as after a suspected cerebral acute ischemic stroke[5]. The various techniques have resulted in a very complicated combination of imaging parameters, which can hamper the acquisition of stable radiomic features. The aim of our study was to investigate the stability of radiomic features from various fast scanned T2-FLAIR images in patients with acute ischemic stroke, and to compare the agreement of the radiomic features with conventional T2-FLAIR as a reference standard
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