Diffusion-weighted imaging-based radiomics for predicting 1-year ischemic stroke recurrence

Wang, Hao; Sun, Yuhan; Zhu, Jie; Zhuang, Yuzhong; Song, Bin

doi:10.3389/fneur.2022.1012896

Cited by 6 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al also predicted 1-year ischemic stroke recurrence in 1003 AIS patients based on MRI images. They obtained similar results [42].…”

Section: Detection Of Ischemic Strokesupporting

confidence: 62%

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

Porto-Álvarez,

Martínez,

Martínez Fernández

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

“…Wang et al also predicted 1-year ischemic stroke recurrence in 1003 AIS patients based on MRI images. They obtained similar results [42].…”

Section: Detection Of Ischemic Strokesupporting

confidence: 62%

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

Porto-Álvarez,

Martínez,

Martínez Fernández

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…There are studies on stroke onset time estimation with radiomics [174]. Finally, some studies are performed to train the radiomic models to predict post-stroke events such as recurrent stroke or epilepsy [175,176,177].…”

Section: Strokementioning

confidence: 99%

Quantitative neuroimaging with handcrafted and deep radiomics in neurological diseases

Lavrova

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

show abstract

MRI-based clinical-radiomics nomogram to predict early neurological deterioration in isolated acute pontine infarction: a two-center study in Northeast China

Wang,

Fu,

Wang

et al. 2024

BMC Neurol

View full text Add to dashboard Cite

Objective To predict the appearance of early neurological deterioration (END) among patients with isolated acute pontine infarction (API) based on magnetic resonance imaging (MRI)-derived radiomics of the infarct site. Methods 544 patients with isolated API were recruited from two centers and divided into the training set (n = 344) and the verification set (n = 200). In total, 1702 radiomics characteristics were extracted from each patient. A support vector machine algorithm was used to construct a radiomics signature (rad-score). Subsequently, univariate and multivariate logistic regression (LR) analysis was adopted to filter clinical indicators and establish clinical models. Then, based on the LR algorithm, the rad-score and clinical indicators were integrated to construct the clinical-radiomics model, which was compared with other models. Results A clinical-radiomics model was established, including the 5 indicators rad-score, age, initial systolic blood pressure, initial National Institute of Health Stroke Scale, and triglyceride. A nomogram was then made based on the model. The nomogram had good predictive accuracy, with an area under the curve (AUC) of 0.966 (95% confidence interval [CI] 0.947–0.985) and 0.920 (95% [CI] 0.873–0.967) in the training and verification sets, respectively. According to the decision curve analysis, the clinical-radiomics model showed better clinical value than the other models. In addition, the calibration curves also showed that the model has excellent consistency. Conclusion The clinical-radiomics model combined MRI-derived radiomics and clinical metrics and may serve as a scoring tool for early prediction of END among patients with isolated API.

show abstract

Diffusion-weighted imaging-based radiomics for predicting 1-year ischemic stroke recurrence

Cited by 6 publications

References 40 publications

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

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

Quantitative neuroimaging with handcrafted and deep radiomics in neurological diseases

MRI-based clinical-radiomics nomogram to predict early neurological deterioration in isolated acute pontine infarction: a two-center study in Northeast China

Contact Info

Product

Resources

About