Automated brain computed tomographic densitometry of early ischemic changes in acute stroke

Stoel, Berend C.; Marquering, Henk A.; Staring, Marius; Beenen, Ludo F. M.; Slump, Cornelis H.; Roos, Yvo B.W.E.M.; Majoie, Charles B. L. M.

doi:10.1117/1.jmi.2.1.014004

Cited by 21 publications

(15 citation statements)

References 40 publications

(37 reference statements)

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“…11,12 Of particular interest, the densitometric analysis enables a quantitative examination of the parenchymal density distribution, thereby enabling subsequent measurements of the proportions of GM and WM showing a normal density. 11,35 If both the deep GM and WM densities decrease in response to ischemic-edematous insults after TBI, then the quantification of the material density of these structures could significantly enhance the prognostic capacity of noncontrast CT images in patients with acute TBI.…”

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confidence: 99%

Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury

Kim

Song

et al. 2019

Journal of Neurosurgery

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OBJECTIVEGray matter (GM) and white matter (WM) are vulnerable to ischemic-edematous insults after traumatic brain injury (TBI). The extent of secondary insult after brain injury is quantifiable using quantitative CT analysis. One conventional quantitative CT measure, the gray-white matter ratio (GWR), and a more recently proposed densitometric analysis are used to assess the extent of these insults. However, the prognostic capacity of the GWR in patients with TBI has not yet been validated. This study aims to test the prognostic value of the GWR and evaluate the alternative parameters derived from the densitometric analysis acquired during the acute phase of TBI. In addition, the prognostic ability of the conventional TBI prognostic models (i.e., IMPACT [International Mission for Prognosis and Analysis of Clinical Trials in TBI] and CRASH [Corticosteroid Randomisation After Significant Head Injury] models) were compared to that of the quantitative CT measures.METHODSThree hundred patients with TBI of varying ages (92 pediatric, 94 adult, and 114 geriatric patients) and admitted between 2008 and 2013 were included in this retrospective cohort study. The normality of the density of the deep GM and whole WM was evaluated as the proportion of CT pixels with Hounsfield unit values of 31–35 for GM and 26–30 for WM on CT images of the entire supratentorial brain. The outcome was evaluated using the Glasgow Outcome Scale (GOS) at discharge (GOS score ≤ 3, n = 100).RESULTSLower proportions of normal densities in the deep GM and whole WM indicated worse outcomes. The proportion of normal WM exhibited a significant prognostic capacity (area under the curve [AUC] = 0.844). The association between the outcome and the normality of the WM density was significant in adult (AUC = 0.792), pediatric (AUC = 0.814), and geriatric (AUC = 0.885) patients. In pediatric patients, the normality of the overall density and the density of the GM were indicative of the outcome (AUC = 0.751). The average GWR was not associated with the outcome (AUC = 0.511). IMPACT and CRASH models showed adequate and reliable performance in the pediatric and geriatric groups but not in the adult group. The highest overall predictive performance was achieved by the densitometry-augmented IMPACT model (AUC = 0.881).CONCLUSIONSBoth deep GM and WM are susceptible to ischemic-edematous insults during the early phase of TBI. The extent of the secondary injury was better evaluated by analyzing the normality of the deep GM and WM rather than by calculating the GWR.

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confidence: 99%

Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury

Kim

Song

et al. 2019

Journal of Neurosurgery

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“…[13][14][15][16][17] In recent years, evidence that automated ASPECTS scoring methods based on machine learning are comparable with expert reading of ASPECTS is accumulating. [18][19][20][21][22][23][24] In this study, we developed an automated ASPECTS scoring system based on machine learning and feature engineering and compared it with expert ASPECTS readings on acute DWI. We introduced multiple highorder computational textural features into our machine learning model and hypothesized that this automated method can determine ASPECTS scores accurately and reliably compared with expert ASPECTS readings on acute DWI.…”

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confidence: 99%

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning

Kuang¹,

Najm²,

Chakraborty³

et al. 2018

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Alberta Stroke Program Early CT Score (ASPECTS) was devised as a systematic method to assess the extent of early ischemic change on noncontrast CT (NCCT) in patients with acute ischemic stroke (AIS). Our aim was to automate ASPECTS to objectively score NCCT of AIS patients. MATERIALS AND METHODS: We collected NCCT images with a 5-mm thickness of 257 patients with acute ischemic stroke (Ͻ8 hours from onset to scans) followed by a diffusion-weighted imaging acquisition within 1 hour. Expert ASPECTS readings on DWI were used as ground truth. Texture features were extracted from each ASPECTS region of the 157 training patient images to train a random forest classifier. The unseen 100 testing patient images were used to evaluate the performance of the trained classifier. Statistical analyses on the total ASPECTS and region-level ASPECTS were conducted. RESULTS: For the total ASPECTS of the unseen 100 patients, the intraclass correlation coefficient between the automated ASPECTS method and DWI ASPECTS scores of expert readings was 0.76 (95% confidence interval, 0.67-0.83) and the mean ASPECTS difference in the Bland-Altman plot was 0.3 (limits of agreement, Ϫ3.3, 2.6). Individual ASPECTS region-level analysis showed that our method yielded ϭ 0.60, sensitivity of 66.2%, specificity of 91.8%, and area under curve of 0.79 for 100 ϫ 10 ASPECTS regions. Additionally, when ASPECTS was dichotomized (Ͼ4 and Յ4), ϭ 0.78, sensitivity of 97.8%, specificity of 80%, and area under the curve of 0.89 were generated between the proposed method and expert readings on DWI. CONCLUSIONS: The proposed automated ASPECTS scoring approach shows reasonable ability to determine ASPECTS on NCCT images in patients presenting with acute ischemic stroke.

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“…One patient did not meet the reperfusion inclusion criteria and was excluded. The 5 patients included had a mean age of 59 (standard deviation (SD) = ±8.8) years, a median National Institute of Health Stroke Scale of 13 (interquartile range, [10][11][12][13][14], and a mean time from symptom onset until CT scan of 4.95 (SD, ±3.5; range, 1.05-10.45) hours. Three patients had Internal Carotid Artery occlusions and two had Middle Cerebral Artery occlusions.…”

Section: Resultsmentioning

confidence: 99%

“…Other previous studies that have exploited asymmetry did not use a voxel‐based reference. Instead, they used as a reference either the entire contralesional hemisphere or large regions defined by the ASPECT scale, a 10‐point scale that describes the extent of early ischemic changes in 10 brain regions …”

Section: Discussionmentioning

confidence: 99%

A Relative Noncontrast CT Map to Detect Early Ischemic Changes in Acute Stroke

Srivatsan

Christensen

Lansberg

2019

Journal of Neuroimaging

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BACKGROUND AND PURPOSE Early ischemic changes on noncontrast computed tomography (NCCT) are often subtle. We developed a novel postprocessing technique that aids in detecting such changes. METHODS NCCT maps were generated that display the relative density difference between corresponding voxels in contralateral hemispheres (ratio maps of the NCCT [rNCCT]). Voxels with a relative density difference below .95 were designated as infarct. We pilot tested the rNCCT for infarct segmentation on 6 consecutive subjects enrolled in the CT Perfusion to predict Response in Ischemic Stroke Project (CRISP) study and applied the inclusion criteria of an adequate quality NCCT and successful endovascular reperfusion. rNCCT infarct segmentation was compared to baseline NCCT, baseline CTP, and day‐5 follow‐up fluid‐attenuated inversion recovery (FLAIR). RESULTS Five of the six selected cases met the inclusion criteria. Their median time from symptom onset to CT was 4.95 hours (standard deviation [SD], ±3.5; range, 1.05‐10.45), and median NIHSS was 13. Early ischemic changes were identified on the rNCCT in all five cases and on the standard NCCT in three of the five cases. Lesions outlined by the rNCCT maps trended toward a better estimation of the day‐5 FLAIR volume (median difference = 6.2 mL) than the ischemic core volumes assessed on baseline CTP (median difference = 51.7 mL) in the four cases with a day‐5 FLAIR (P = .1). CONCLUSION In this proof‐of‐concept study, the rNCCT appears promising for detecting and quantifying early ischemic changes. These findings should be confirmed in a larger cohort.

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Automated brain computed tomographic densitometry of early ischemic changes in acute stroke

Cited by 21 publications

References 40 publications

Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury

Changes in the gray and white matter of patients with ischemic-edematous insults after traumatic brain injury

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning

A Relative Noncontrast CT Map to Detect Early Ischemic Changes in Acute Stroke

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