2022
DOI: 10.1007/s11547-022-01505-5
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CT angiography-based radiomics as a tool for carotid plaque characterization: a pilot study

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Cited by 19 publications
(15 citation statements)
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References 46 publications
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“…This is due to several factors, including a lack of harmonization of imaging protocols, clinical validation issues, and an overall poor scientific quality of the studies in the field [ 43 , 45 , 103 , 104 ]. In line with this scenario, a RQS of 27.8% (range 22.2–38.9%) was calculated for the 8 articles retrieved in this review ( Table 2 ), consistent with other reviews focused on radiomics [ 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 ].…”
Section: Discussionsupporting
confidence: 85%
“…This is due to several factors, including a lack of harmonization of imaging protocols, clinical validation issues, and an overall poor scientific quality of the studies in the field [ 43 , 45 , 103 , 104 ]. In line with this scenario, a RQS of 27.8% (range 22.2–38.9%) was calculated for the 8 articles retrieved in this review ( Table 2 ), consistent with other reviews focused on radiomics [ 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 ].…”
Section: Discussionsupporting
confidence: 85%
“…Radiomics is a rapidly evolving field in medical imaging, and studies have confirmed its applications and potential in guiding clinical decision-making and precision medicine [ 25 ]. Radiomics-based carotid artery CTA methodologies are an objective and effective means of assessing carotid atherosclerotic plaques and stratifying related risk [ 35 ]. However, the accuracy and reliability of CT-based radiomic analyses are heavily dependent on the quality of acquired images, and routine carotid artery CTA images are usually of low resolution, possibly leading to inaccurate feature extraction and subsequent risk evaluation [ 24 ].…”
Section: Discussionmentioning
confidence: 99%
“…Further, LR, LDA and ANN predictive models were constructed based on the PET quantitative indices, and ROC analysis showed that the LR model had greater predictive value in identifying epileptogenic tubers than the LDA and ANN models. LR, a sensitive and stable binary prediction machine learning method, is widely used in feature-based classification, and it has been applied to single-neuron recordings from depth-electrode microwires to predict seizure onset zones [34,35]. In addition, a predictive nomogram was constructed to make accurate localization assessments to quantitatively predict epileptogenic tubers in a personalized way.…”
Section: Discussionmentioning
confidence: 99%