Computed Tomography Angiography-Based Thrombus Radiomics for Predicting the Time Since Stroke Onset

Cheng, Yue; Wan, Sunli; Wu, Wenjuan; Chen, Fang‐Ming; Jiang, Jingxuan; Cai, Dongmei; Bao, Zhongyuan; Li, Yuehua; Zhang, Lei

doi:10.1016/j.acra.2022.12.032

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…They concluded that there was no significant difference between the radiomics and combined models. They also concluded that radiomics based on RFs obtained from CTA images can estimate TSS in patients with AIS [58]. Yao et al also developed a CTbased radiomic model to classify the onset time of basal ganglia infarcts.…”

Section: Time Since Stroke Predictionmentioning

confidence: 99%

How Can Radiomics Help the Clinical Management of Patients with Acute Ischemic Stroke?

Porto-Álvarez,

Martínez,

Martínez Fernández

et al. 2023

Applied Sciences

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Acute ischemic stroke (AIS) is the loss of neurological function due to a sudden reduction in cerebral blood flow and is a leading cause of disability and death worldwide. The field of radiological imaging has experienced growth in recent years, which could be boosted by the advent of artificial intelligence. One of the latest innovations in artificial intelligence is radiomics, which is based on the fact that a large amount of quantitative data can be extracted from radiological images, from which patterns can be identified and associated with specific pathologies. Since its inception, radiomics has been particularly associated with the field of oncology and has shown promising results in a wide range of clinical situations. The performance of radiomics in non-tumour pathologies has been increasingly explored in recent years, and the results continue to be promising. The aim of this review is to explore the potential applications of radiomics in AIS patients and to theorize how radiomics may change the paradigm for these patients in the coming years.

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Section: Time Since Stroke Predictionmentioning

confidence: 99%