The role of computed tomography angiogram in intracranial hemorrhage. Do the benefits justify the known risks in everyday practice?

Alshumrani, Ghazi; nasser, Bassam Al abo; Alzawani, Abdulaziz; Alsabaani, Abdullah; Shehata, Shehata; Alhazzani, Adel

doi:10.1016/j.clineuro.2020.106379

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Along these lines, Alshumrani et al found that although 68% of CT scans showed hemorrhage, nearly 58% of CTA examinations were normal or did not show associated abnormalities. This suggests that in most cases, CTA cannot determine the cause of the bleeding 9 . Therefore, radiomics models can significantly improve the diagnosis and help in imaging-based investigations and treatment strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, some studies have investigated magnetic susceptibility weighted imaging or the 99m Tc-MIBI SPECT techniques for ICH diagnosis, but the accuracy rates have reached only 68.83% and 65.5%, respectively. Other imaging approaches in the clinic have involved CT angiography (CTA), contrast-enhanced CT, or contrast-enhanced magnetic resonance imaging (MRI) 8 , but their diagnostic accuracy has been reported as only around 43% 9 . The imaging-based diagnosis of secondary ICH can further complicate clinical procedures because the enhanced signal characteristics of these lesions can be obscured by high signal attenuation or various hematoma-based T 1 -weighted MRI signal heterogeneities 10 .…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage

Lyu

Sun

et al. 2023

Sci Rep

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It is challenging to distinguish between primary and secondary intracranial hemorrhage (ICH) purely by imaging data, and the two forms of ICHs are treated differently. This study aims to evaluate the potential of CT-based machine learning to identify the etiology of ICHs and compare the effectiveness of two regions of interest (ROI) sketching methods. A total of 1702 radiomic features were extracted from the CT brain images of 238 patients with acute ICH. We used the Select K Best method, least absolute shrinkage, and selection operator logistic regression to select the most discriminable features with a support vector machine to build a classifier model. Then, a ten-fold cross-validation strategy was employed to evaluate the performance of the classifier. From all quantitative CT-based imaging features obtained by two sketch methods, eighteen features were selected respectively. The radiomics model outperformed radiologists in distinguishing between primary and secondary ICH in both the volume of interest and the three-layer ROI sketches. As a result, a machine learning-based CT radiomics model can improve the accuracy of identifying primary and secondary ICH. A three-layer ROI sketch can identify primary versus secondary ICH based on the CT radiomics method.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage

Lyu

Sun

et al. 2023

Sci Rep

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“…A specialized variant of CT is CT angiography (CTA) which augments traditional CT with intravenous contrast to produce images of blood vessels and tissues. Due to its ability to clearly track the course of blood products, CTA is often used as the first-line assessment of intracranial vasculature integrity, aneurysms, and hemorrhage [31] . Additionally, CTA may be used as radiographic support for diagnosing brain death following catastrophic brain injury [32] .…”

Section: Ct Angiographymentioning

confidence: 99%

The Role of Advanced Imaging in Neurosurgical Diagnosis

et al. 2023

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Neurosurgery as a specialty has developed at a rapid pace as a result of the continual advancements in neuroimaging modalities. With more sophisticated imaging options available to the modern neurosurgeon, diagnoses become more accurate and at a faster rate, allowing for greater surgical planning and precision. Herein, the authors review the current heavily used imaging modalities within neurosurgery, weighing their strengths and weaknesses, and provide a look into new advances and imaging options within the field. Of the many imaging modalities currently available to the practicing neurosurgeon, magnetic resonance imaging (MRI), computed tomography, positron emission tomography, and ultrasonography are used most heavily within the field for appropriate diagnosis of neuropathologies in question. For each, their strengths are weighed regarding appropriate capabilities in accurate diagnosis of cranial or spinal lesions. Reasoning for choosing one over the other for various pathologies is also reviewed. Current limitations of each is also assessed, providing insight for possible improvement for each. New advancements in imaging options are subsequently reviewed for best uses within neurosurgery, including the new utilization of FIESTA sequencing, glymphatic mapping, black-blood MRI, and functional MRI. The specialty of neurosurgery will continue to heavily rely on improvements within imaging options available for improved diagnosis and greater surgical outcomes for the patients treated. The synthesis of techniques provided herein may provide meaningful guidance for neurosurgeons in effectively diagnosing neurological pathologies while also helping guide future efforts in neuroimaging developments.

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Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning

Kim

Koo

Lee

et al. 2021

J. Korean Phys. Soc.

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The role of computed tomography angiogram in intracranial hemorrhage. Do the benefits justify the known risks in everyday practice?

Cited by 4 publications

References 21 publications

Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage

Machine learning-based CT radiomics model to discriminate the primary and secondary intracranial hemorrhage

The Role of Advanced Imaging in Neurosurgical Diagnosis

Cerebral hemorrhage detection and localization with medical imaging for cerebrovascular disease diagnosis and treatment using explainable deep learning

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