ArticleJournal of neurology2022
MRI radiomic features-based machine learning approach to classify ischemic stroke onset time.
Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- The Value of Applying Machine Learning in Predicting the Time of Symptom Onset in Stroke Patients: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2023Pooled it
- Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.Neuroradiology · 2026Article
- Automated DWI-FLAIR mismatch assessment in stroke using DWI only.European stroke journal · 2026Article
- Automated DWI-FLAIR mismatch assessment in stroke using DWI only.European stroke journal · 2025Article
- Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study.Scientific reports · 2025Article
- A clinical-radiomics nomogram to predict early neurological deterioration in patients with stroke undergoing intravenous thrombolysis.Journal of the Chinese Medical Association : JCMA · 2025Article
- Nomogram to predict 1-year cognitive decline after stent placement for unruptured intracranial aneurysms.iScience · 2025Article
- Artificial intelligence in medical imaging: From task-specific models to large-scale foundation models.Chinese medical journal · 2025Review
- The impracticality of radiomics research in acute ischemic stroke: from the perspective of primary healthcare institutions.Frontiers in neurology · 2025Review
- A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study.European radiology · 2024Article
- Application of radiomics in ischemic stroke.The Journal of international medical research · 2024Review
- Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables.Frontiers in medicine · 2024Article
- DWI-Based Radiomics Predicts the Functional Outcome of Endovascular Treatment in Acute Basilar Artery Occlusion.AJNR. American journal of neuroradiology · 2023Article
- Identifying patients with acute ischemic stroke within a 6-h window for the treatment of endovascular thrombectomy using deep learning and perfusion imaging.Frontiers in medicine · 2023Article
- Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome.Diagnostics (Basel, Switzerland) · 2022Article
- Prognosis of ischemic stroke predicted by machine learning based on multi-modal MRI radiomics.Frontiers in psychiatry · 2022Article
- Radiomics features of DSC-PWI in time dimension may provide a new chance to identify ischemic stroke.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
Abstract
purposeWe aimed to investigate the ability of MRI radiomics features-based machine learning (ML) models to classify the time since stroke onset (TSS), which could aid in stroke assessment and treatment options.
methodsThis study involved 84 patients with acute ischemic stroke due to anterior circulation artery occlusion (51 in the training cohort and 33 in the independent test cohort). Region of infarct segmentation was manually outlined by 3D-slicer software. Image processing including registration, normalization and radiomics features calculation were done in R (version 3.6.1). A total of 4312 radiomic features from each image sequence were captured and used in six ML models to estimate stroke onset time for binary classification (≤ 4.5 h). Receiver-operating characteristic curve (ROC) and other parameters were calculated to evaluate the performance of the models in both training and test cohorts.
resultsTwelve radiomics and six clinic features were selected to construct the ML models for TSS classification. The deep learning model-based DWI/ADC radiomic features performed the best for binary TSS classification in the independent test cohort, with an AUC of 0.754, accuracy of 0.788, sensitivity of 0.952, specificity of 0.500, positive predictive value of 0.769, and negative predictive value of 0.857, respectively. Furthermore, adding clinical information did not improve the performance of the DWI/ADC-based deep learning model. The TSS prediction models can be visited at: http://123.57.65.199:3838/deeptss/ .
conclusionsA unique deep learning model based on DWI/ADC radiomic features was constructed for TSS classification, which could aid in decision making for thrombolysis in patients with unknown stroke onset.
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Registered trials
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