ArticleJournal of personalized medicine2022
Novel and Efficient Quantitative Posterior-Circulation-Structure-Based Scale via Noncontrast CT to Predict Ischemic Stroke Prognosis: A Retrospective Study.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Automatic prediction of stroke treatment outcomes: latest advances and perspectives.Biomedical engineering letters · 2025Review
- Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.European radiology experimental · 2025Article
- Potential and limitations of computed tomography images as predictors of the outcome of ischemic stroke events: a review.Frontiers in stroke · 2023Review
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6 authors.
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Abstract
(1) Background: Posterior circulation ischemic stroke has high mortality and disability rates and requires an early prediction prognosis to provide the basis for an interventional approach. Current quantitative measures are only able to accurately assess the prognosis of patients using magnetic resonance imaging (MRI). However, it is difficult to obtain MRI images in critically urgent cases. Therefore, the development of a noncontrast CT-based rapid-assist tool is needed to enhance the value of the clinical application. (2) Objective: This study aimed to develop an auxiliary-annotating noncontrast CT-efficient tool, which is based on a deep learning model, to provide a quantitative scale and the prognosis of posterior circulation ischemic stroke patients. (3) Methods: A total of 31 patients with posterior circulation ischemic stroke, diagnosed in the stroke registry at the Tri-Service General Hospital from November 2019 to July 2020, were included in the study, with a total of 578 CT images collected from noncontrast CT and MRI that were ≤ 3 days apart. A 5-fold cross validation was used to develop an image segmentation model to identify nine posterior circulation structures, and intersection over union (IoU) was used to assess the ability of the model to identify each structure. A quantitative score was integrated to assess the importance of the proportion of ischemic lesions in each posterior circulation structure, and the ROC curve was compared with the semiquantitative score for prognostic power. The prognoses of the patients were defined into two groups of 18 patients. An mRS score of 0-2 at discharge was defined as a good prognosis, while an mRS score of 3-6 was deemed to be a poor prognosis. (4) Results: The performance of the image segmentation model for identifying the nine posterior circulation structures in noncontrast CT images was evaluated. The IoU of the left cerebellum was 0.78, the IoU of the right cerebellum was 0.79, the IoU of the left occipital lobe was 0.74, the IoU of the right occipital lobe was 0.68, the IoU of the left thalamus was 0.73, the IoU of the right thalamus was 0.75, the IoU of the medulla oblongata was 0.82, and the IoU of the midbrain was 0.83. The prognostic AUC of posterior circulation patients predicted using a quantitative integrated score was 0.74, which was significantly higher than that of the pc-ASPECTS (AUC = 0.63,
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