Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival

Zaman, Saif; Dierksen, Fiona; Knapp, Avery; Haider, Stefan P.; Abou Karam, Gaby; Qureshi, Adnan I.; Falcone, Guido J.; Sheth, Kevin N.; Payabvash, Seyedmehdi

doi:10.3390/diagnostics14090944

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…The above stroke therapies serve for the treatment of ischemic stroke, which accounts for approximately 80% of cerebrovascular events [4]. There are also several therapies to treat hemorrhagic stroke, especially since the mortality rate of acute intracerebral hemorrhage can reach 40% [122]. One of them is to evacuate the hemorrhage surgically by performing craniotomy.…”

Section: Taxonomy Of Stroke Treatmentmentioning

confidence: 99%

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Nowinski

2024

Diagnostics

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Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.

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Section: Taxonomy Of Stroke Treatmentmentioning

confidence: 99%

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Nowinski

2024

Diagnostics

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Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography

Tran,

Desser,

Zeevi

et al. 2024

Applied Sciences

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Hematoma expansion (HE) is an independent predictor of poor outcomes and a modifiable treatment target in intracerebral hemorrhage (ICH). Evaluating HE in large datasets requires segmentation of hematomas on admission and follow-up CT scans, a process that is time-consuming and labor-intensive in large-scale studies. Automated segmentation of hematomas can expedite this process; however, cumulative errors from segmentation on admission and follow-up scans can hamper accurate HE classification. In this study, we combined a tandem deep-learning classification model with automated segmentation to generate probability measures for false HE classifications. With this strategy, we can limit expert review of automated hematoma segmentations to a subset of the dataset, tailored to the research team’s preferred sensitivity or specificity thresholds and their tolerance for false-positive versus false-negative results. We utilized three separate multicentric cohorts for cross-validation/training, internal testing, and external validation (n = 2261) to develop and test a pipeline for automated hematoma segmentation and to generate ground truth binary HE annotations (≥3, ≥6, ≥9, and ≥12.5 mL). Applying a 95% sensitivity threshold for HE classification showed a practical and efficient strategy for HE annotation in large ICH datasets. This threshold excluded 47–88% of test-negative predictions from expert review of automated segmentations for different HE definitions, with less than 2% false-negative misclassification in both internal and external validation cohorts. Our pipeline offers a time-efficient and optimizable method for generating ground truth HE classifications in large ICH datasets, reducing the burden of expert review of automated hematoma segmentations while minimizing misclassification rate.

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Radiomic Features of Acute Cerebral Hemorrhage on Non-Contrast CT Associated with Patient Survival

Cited by 2 publications

References 28 publications

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography

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