Spatial Resolution and the Magnitude of Infarct Volume Measurement Error in DWI in Acute Ischemic Stroke

How Do Quantitative Tissue Imaging Outcomes in Acute Ischemic Stroke Relate to Clinical Outcomes?

BACKGROUND AND PURPOSE

Infarct volume and other imaging markers are increasingly used as surrogate measures for clinical outcome in acute ischemic stroke research, but how improvements in these imaging surrogates translate into better clinical outcomes is currently unclear. We investigated how changes in infarct volume at 24 hours alter the probability of achieving good clinical outcome (modified Rankin Scale [mRS] 0-2).

METHODS

Data are from endovascular thrombectomy patients from the randomized controlled ESCAPE-NA1 (Efficacy and Safety of Nerinetide for the Treatment of Acute Ischaemic Stroke) trial. Infarct volume at 24 hours was manually segmented on non-contrast computed tomography or diffusion-weighted magnetic resonance imaging. Probabilities of achieving good outcome based on infarct volume were obtained from a multivariable logistic regression model. The probability of good outcome was plotted against infarct volume using linear spline regression.

RESULTS

A total of 1,099 patients were included in the analysis (median final infarct volume 24.9 mL [interquartile range: 6.6-92.2]). The relationship between total infarct volume and good outcome probability was nearly linear for infarct volumes between 0 mL and 250 mL. In this range, a 10% increase in the probability of achieving mRS 0-2 required a decrease in infarct volume of approximately 34.0 mL (95% confidence interval: -32.5 to -35.6). At infarct volumes above 250 mL, the probability of achieving mRS 0-2 probability was near zero. The relationships of tissue-specific infarct volumes and parenchymal hemorrhage volume generally showed similar patterns, although variability was high.

CONCLUSION

There seems to be a near-linear association between total infarct volume and probability of achieving good outcome for infarcts up to 250 mL, whereas patients with infarct volumes greater than 250 mL are highly unlikely to have a favorable outcome.

A Review on Adjunctive Therapies for Endovascular Treatment in Acute Ischemic Stroke

Endovascular treatment (EVT) has revolutionized the management of acute ischemic stroke (AIS), but almost half of patients undergoing EVT do not achieve a good outcome. Adjunctive therapies have been proposed to improve the outcomes of EVT in AIS. This review aims to summarize the current evidence on the use of adjunctive therapies in EVT for AIS, including antithrombotic agents, intra-arterial thrombolytics, cerebroprotective agents, normobaric oxygen, and hypothermia. Several adjunctive therapies have shown promise in improving the outcomes of EVT in AIS, but phase 3 clinical trials are needed to establish clinical efficacy. We summarize the advantages and disadvantages of adjunctive EVT treatments and outline the challenges that each of these therapies will face before being adopted in clinical practice.

Automated multimodal segmentation of acute ischemic stroke lesions on clinical MR images

Magnetic resonance (MR) imaging (MRI) is commonly used to diagnose, assess and monitor stroke. Accurate and timely segmentation of stroke lesions provides the anatomico-structural information that can aid physicians in predicting prognosis, as well as in decision making and triaging for various rehabilitation strategies. To segment stroke lesions, MR protocols, including diffusion-weighted imaging (DWI) and T2-weighted fluid attenuated inversion recovery (FLAIR) are often utilized. These imaging sequences are usually acquired with different spatial resolutions due to time constraints. Within the same image, voxels may be anisotropic, with reduced resolution along slice direction for diffusion scans in particular. In this study, we evaluate the ability of 2D and 3D U-Net Convolutional Neural Network (CNN) architectures to segment ischemic stroke lesions using single contrast (DWI) and dual contrast images (T2w FLAIR and DWI). The predicted segmentations correlate with post-stroke motor outcome measured by the National Institutes of Health Stroke Scale (NIHSS) and Fugl-Meyer Upper Extremity (FM-UE) index based on the lesion loads overlapping the corticospinal tracts (CST), which is a neural substrate for motor movement and function. Although the four methods performed similarly, the 2D multimodal U-Net achieved the best results with a mean Dice of 0.737 (95% CI: 0.705, 0.769) and a relatively high correlation between the weighted lesion load and the NIHSS scores (both at baseline and at 90 days). A monotonically constrained quintic polynomial regression yielded R2 = 0.784 and 0.875 for weighted lesion load versus baseline and 90-Days NIHSS respectively, and better corrected Akaike information criterion (AICc) scores than those of the linear regression. In addition, using the quintic polynomial regression model to regress the weighted lesion load to the 90-Days FM-UE score results in an R2 of 0.570 with a better AICc score than that of the linear regression. Our results suggest that the multi-contrast information enhanced the accuracy of the segmentation and the prediction accuracy for upper extremity motor outcomes. Expanding the training dataset to include different types of stroke lesions and more data points will help add a temporal longitudinal aspect and increase the accuracy. Furthermore, adding patient-specific data may improve the inference about the relationship between imaging metrics and functional outcomes.

A2DS2 Score Combined With Clinical and Neuroimaging Factors Better Predicts Stroke-Associated Pneumonia in Hyperacute Cerebral Infarction

Objective

To investigate the predictors of stroke-associated pneumonia (SAP) and poor functional outcome in patients with hyperacute cerebral infarction (HCI) by combining clinical factors, laboratory tests and neuroimaging features.

Methods

We included 205 patients with HCI from November 2018 to December 2019. The diagnostic criterion for SAP was occurrence within 7 days of the onset of stroke. Poor outcome was defined as a functional outcome based on a 3-months MRS score >3. The relationship of demographic, laboratory and neuroimaging variables with SAP and poor outcome was investigated using univariate and multivariate analyses.

Results

Fifty seven (27.8%) patients were diagnosed with SAP and 40 (19.5%) developed poor outcomes. A²DS² score (OR = 1.284; 95% CI: 1.048–1.574; P = 0.016), previous stroke (OR = 2.630; 95% CI: 1.122–6.163; P = 0.026), consciousness (OR = 2.945; 95% CI: 1.514–5.729; P < 0.001), brain atrophy (OR = 1.427; 95% CI: 1.040–1.959; P = 0.028), and core infarct volume (OR = 1.715; 95% CI: 1.163–2.528; P = 0.006) were independently associated with the occurrence of SAP. Therefore, we combined these variables into a new SAP prediction model with the C-statistic of 0.84 (95% CI: 0.78–0.90). Fasting plasma glucose (OR = 1.404; 95% CI: 1.202–1.640; P < 0.001), NIHSS score (OR = 1.088; 95% CI: 1.010–1.172; P = 0.026), previous stroke (OR = 4.333; 95% CI: 1.645–11.418; P = 0.003), SAP (OR = 3.420; 95% CI: 1.332–8.787; P = 0.011), basal ganglia-dilated perivascular spaces (BG-dPVS) (OR = 2.124; 95% CI: 1.313–3.436; P = 0.002), and core infarct volume (OR = 1.680; 95% CI: 1.166–2.420; P = 0.005) were independently associated with poor outcome. The C-statistic of the outcome model was 0.87 (95% CI: 0.81–0.94). Furthermore, the SAP model significantly improved discrimination and net benefit more than the A²DS² scale, with a C-statistic of 0.76 (95% CI: 0.69–0.83).

Conclusions

After the addition of neuroimaging features, the models exhibit good differentiation and calibration for the prediction of the occurrence of SAP and the development of poor outcomes in HCI patients. The SAP model could better predict the SAP, representing a helpful and valid tool to obtain a net benefit compared with the A²DS² scale.

Variability assessment of manual segmentations of ischemic lesion volume on 24-h non-contrast CT

PURPOSE

Infarct lesion volume (ILV) may serve as an imaging biomarker for clinical outcomes in the early post-treatment stage in patients with acute ischemic stroke. The aim of this study was to evaluate the inter- and intra-rater reliability of manual segmentation of ILV on follow-up non-contrast CT (NCCT) scans.

METHODS

Fifty patients from the Prove-IT study were randomly selected for this analysis. Three raters manually segmented ILV on 24-h NCCT scans, slice by slice, three times. The reference standard for ILV was generated by the Simultaneous Truth And Performance Level estimation (STAPLE) algorithm. Intra- and inter-rater reliability was evaluated, using metrics of intraclass correlation coefficient (ICC) regarding lesion volume and the Dice similarity coefficient (DSC).

RESULTS

Median age of the 50 subjects included was 74.5 years (interquartile range [IQR] 67-80), 54% were women, median baseline National Institutes of Health Stroke Scale was 18 (IQR 11-22), median baseline ASPECTS was 9 (IQR 6-10). The mean reference standard ILV was 92.5 ml (standard deviation (SD) ± 100.9 ml). The manually segmented ILV ranged from 88.2 ± 91.5 to 135.5 ± 119.9 ml (means referring to the variation between readers, SD within readers). Inter-rater ICC was 0.83 (95%CI: 0.76-0.88); intra-rater ICC ranged from 0.85 (95%CI: 0.72-0.92) to 0.95 (95%CI: 0.91-0.97). The mean DSC among the three readers ranged from 65.5 ± 22.9 to 76.4 ± 17.1% and the mean overall DSC was 72.8 ± 23.0%.

CONCLUSION

Manual ILV measurements on follow-up CT scans are reliable to measure the radiological outcome despite some variability.

Strength of Association between Infarct Volume and Clinical Outcome Depends on the Magnitude of Infarct Size: Results from the ESCAPE-NA1 Trial

BACKGROUND AND PURPOSE

Infarct volume is an important predictor of clinical outcome in acute stroke. We hypothesized that the association of infarct volume and clinical outcome changes with the magnitude of infarct size.

MATERIALS AND METHODS

Data were derived from the Safety and Efficacy of Nerinetide in Subjects Undergoing Endovascular Thrombectomy for Stroke (ESCAPE-NA1) trial, in which patients with acute stroke with large-vessel occlusion were randomized to endovascular treatment plus either nerinetide or a placebo. Infarct volume was manually segmented on 24-hour noncontrast CT or DWI. The relationship between infarct volume and good outcome, defined as mRS 0-2 at 90 days, was plotted. Patients were categorized on the basis of visual grouping at the curve shoulders of the infarct volume/outcome plot. The relationship between infarct volume and adjusted probability of good outcome was fitted with linear or polynomial functions as appropriate in each group.

RESULTS

We included 1099 individuals in the study. Median infarct volume at 24 hours was 24.9 mL (interquartile range [IQR] = 6.6-92.2 mL). On the basis of the infarct volume/outcome plot, 4 infarct volume groups were defined (IQR = 0-15 mL, 15.1-70 mL, 70.1-200 mL, >200 mL). Proportions of good outcome in the 4 groups were 359/431 (83.3%), 219/337 (65.0%), 71/201 (35.3%), and 16/130 (12.3%), respectively. In small infarcts (IQR = 0-15 mL), no relationship with outcome was appreciated. In patients with intermediate infarct volume (IQR = 15-200 mL), there was progressive importance of volume as an outcome predictor. In infarcts of > 200 mL, outcomes were overall poor.

CONCLUSIONS

The relationship between infarct volume and clinical outcome varies nonlinearly with the magnitude of infarct size. Infarct volume was linearly associated with decreased chances of achieving good outcome in patients with moderate-to-large infarcts, but not in those with small infarcts. In very large infarcts, a near-deterministic association with poor outcome was seen.