Image Patch-Based Net Water Uptake and Radiomics Models Predict Malignant Cerebral Edema After Ischemic Stroke

Radiomics Studies on Ischemic Stroke and Carotid Atherosclerotic Disease: A Reporting Quality Assessment

Objective: To assess the reporting quality of radiomics studies on ischemic stroke, intracranial and carotid atherosclerotic disease using the Image Biomarker Standardization Initiative (IBSI) reporting guidelines with the aim of finding avenues of improvement for future publications. Method: PubMed database was searched to identify relevant radiomics studies. Of 560 articles, 41 original research articles were included in this analysis. Based on IBSI radiomics reporting guidelines, checklists for CT-based and MRI-based studies were created to allow a structured and comprehensive evaluation of each study's adherence to these guidelines. Results: The main topics covered included radiomics studies were ischemic stroke, intracranial artery disease, and carotid atherosclerotic disease. The reporting checklist median score was 17/40 for the 20 CT-based radiomics studies and 22.5/50 for the 20 MRI-based studies. Basic items like imaging modality, region of interest, and image biomarker set utilized were included in all studies. However, details regarding image acquisition and reconstruction, post-acquisition image processing, and image biomarkers computation were inconsistently detailed across studies. Conclusion: The overall reporting quality of the included radiomics studies was suboptimal. These findings underscore a pressing need for improved reporting practices in radiomics research, to ensure validation and reproducibility of results. Our study provides insights into current reporting standards and highlights specific areas where adherence to IBSI guidelines could be significantly improved.

Net water uptake as a predictive neuroimaging marker for acute ischemic stroke outcomes: a meta-analysis

OBJECTIVE

To assess the role of net water uptake (NWU) in predicting outcomes in acute ischemic stroke (AIS) patients.

METHODS

A systematic review and meta-analysis were performed, adhering to established guidelines. The search covered PubMed, Scopus, Web of Science, and Embase databases until July 1, 2023. Eligible studies reporting quantitative ischemic lesion NWU in admission CT scans of AIS patients, stratified based on outcomes, were included. Data analysis was performed using R software version 4.2.1.

RESULTS

Incorporating 17 original studies with 2217 AIS patients, NWU was significantly higher in patients with poor outcomes compared to those with good outcomes (difference of medians: 5.06, 95% CI: 3.00-7.13, p < 0.001). Despite excluding one outlier study, considerable heterogeneity persisted among the included studies (I2 = 90.8%). The meta-regression and subgroup meta-analyses demonstrated significantly higher NWU in patients with poor functional outcome, as assessed by modified Rankin Scale (difference of medians: 3.83, 95% CI: 1.98-5.68, p < 0.001, I2 = 72.9%), malignant edema/infarct (difference of medians: 8.30, 95% CI: 4.01-12.58, p < 0.001, I2 = 95.6%), and intracranial hemorrhage (difference of medians: 5.43, 95% CI: 0.44-10.43, p = 0.03, I2 = 91.1%).

CONCLUSION

NWU on admission CT scans shows promise as a predictive marker for outcomes in AIS patients. Prospective, multicenter trials with standardized, automated NWU measurement are crucial for robustly predicting diverse clinical outcomes.

CLINICAL RELEVANCE STATEMENT

The potential of net water uptake as a biomarker for predicting outcomes in acute ischemic stroke patients holds significant promise. Further validation through additional research could lead to its integration into clinical practice, potentially improving the accuracy of clinical decision-making and allowing for the development of more precise patient care strategies.

KEY POINTS

• Net water uptake, a CT-based biomarker, quantifies early brain edema after acute ischemic stroke. • Net water uptake is significantly higher in poor outcome acute ischemic stroke patients. • Net water uptake on CT scans holds promise in predicting diverse acute ischemic stroke outcomes.

Application of radiomics in ischemic stroke

In recent years, radiomics has emerged as a novel research methodology that plays a crucial role in the diagnosis and treatment of ischemic stroke. By integrating multimodal medical imaging techniques such as computed tomography and magnetic resonance imaging, radiomics offers in-depth insights into aspects such as the extent of brain tissue damage and hemodynamics. These data help physicians to accurately assess patient condition, select optimal treatment strategies, and predict recovery trajectories and long-term prognoses, thereby enhancing treatment efficacy and reducing the risk of complications. With the anticipated further advancements in radiomic technology, this methodology has great potential for expanded applications in the early detection, treatment, and prognosis of ischemic stroke. The present narrative review explores the burgeoning field of radiomics and its transformative impact on ischemic stroke.

Early venous filling after mechanical thrombectomy in acute ischemic stroke due to large vessel occlusion in anterior circulation

BACKGROUND

The significance of early venous filling (EVF) after mechanical thrombectomy (MT) in acute ischemic stroke (AIS) is not fully understood. In this study, we aimed to investigate the impact of EVF after MT.

METHODS

From January 2019 to May 2022, AIS patients with successful recanalization (modified Thrombolysis in Cerebral Infarction score (mTICI) ≥2b) after MT were retrospectively reviewed. EVF was evaluated on final digital subtraction angiography runs after successful recanalization and was categorized into phase subgroups (arterial phase and capillary phase) and pathway subgroups (cortical veins subgroup and thalamostriate veins subgroup), respectively. The impact of EVF subgroups on functional outcomes after successful recanalization were both investigated.

RESULTS

A total of 349 patients achieving successful recanalization after MT were included, including 45 patients in the EVF group and 304 patients in the non-EVF group. Multivariable logistic regression analysis showed the EVF group had a higher rate of intracranial hemorrhage (ICH; 66.7% vs 22%, adjusted odds ratio (aOR) 6.805, 95% CI 3.389 to 13.662, P<0.001), symptomatic ICH (sICH; 28.9% vs 4.9%, aOR 6.011, 95% CI 2.493 to 14.494, P<0.001) and malignant cerebral edema (MCE; 20% vs 6.9%, aOR 2.682, 95% CI 1.086 to 6.624, P=0.032) than the non-EVF group. Furthermore, the cortical veins subgroup of EVF had a higher rate of mortality than the thalamostriate veins subgroup (37.5% vs 10.3%, P=0.029).

CONCLUSIONS

EVF is independently associated with ICH, sICH and MCE after successful recanalization of MT, but not with favorable outcome and mortality.

MRI-based clinical-radiomics nomogram to predict early neurological deterioration in isolated acute pontine infarction: a two-center study in Northeast China

OBJECTIVE

To predict the appearance of early neurological deterioration (END) among patients with isolated acute pontine infarction (API) based on magnetic resonance imaging (MRI)-derived radiomics of the infarct site.

METHODS

544 patients with isolated API were recruited from two centers and divided into the training set (n = 344) and the verification set (n = 200). In total, 1702 radiomics characteristics were extracted from each patient. A support vector machine algorithm was used to construct a radiomics signature (rad-score). Subsequently, univariate and multivariate logistic regression (LR) analysis was adopted to filter clinical indicators and establish clinical models. Then, based on the LR algorithm, the rad-score and clinical indicators were integrated to construct the clinical-radiomics model, which was compared with other models.

RESULTS

A clinical-radiomics model was established, including the 5 indicators rad-score, age, initial systolic blood pressure, initial National Institute of Health Stroke Scale, and triglyceride. A nomogram was then made based on the model. The nomogram had good predictive accuracy, with an area under the curve (AUC) of 0.966 (95% confidence interval [CI] 0.947-0.985) and 0.920 (95% [CI] 0.873-0.967) in the training and verification sets, respectively. According to the decision curve analysis, the clinical-radiomics model showed better clinical value than the other models. In addition, the calibration curves also showed that the model has excellent consistency.

CONCLUSION

The clinical-radiomics model combined MRI-derived radiomics and clinical metrics and may serve as a scoring tool for early prediction of END among patients with isolated API.

Net water uptake within the ischemic penumbra predicts the presence of the midline shift in patients with acute ischemic stroke

Objective

The study aimed to explore the association between midline shift (MLS) and net water uptake (NWU) within the ischemic penumbra in acute ischemic stroke patients.

Methods

This was a retrospective cohort study that examined patients with anterior circulation stroke. Net water uptake within the acute ischemic core and penumbra was calculated using data from admission multimodal CT scans. The primary outcome was severe cerebral edema measured by the presence of MLS on 24 to 48 h follow-up CT scans. The presence of a significant MLS was defined by a deviation of the septum pellucidum from the midline on follow-up CT scans of at least 3 mm or greater due to the mass effect of ischemic edema. The net water uptake was compared between patients with and without MLS, followed by logistic regression analyses and receiver operating characteristics (ROCs) to assess the predictive power of net water uptake in MLS.

Results

A total of 133 patients were analyzed: 50 patients (37.6%) with MLS and 83 patients (62.4%) without. Compared to patients without MLS, patients with MLS had higher net water uptake within the core [6.8 (3.2-10.4) vs. 4.9 (2.2-8.1), P = 0.048] and higher net water uptake within the ischemic penumbra [2.9 (1.8-4.3) vs. 0.2 (-2.5-2.7), P < 0.001]. Penumbral net water uptake had higher predictive performance than net water uptake of the core in MLS [area under the curve: 0.708 vs. 0.603, p < 0.001]. Moreover, the penumbral net water uptake predicted MLS in the multivariate regression model, adjusting for age, sex, admission National Institutes of Health Stroke Scale (NIHSS), diabetes mellitus, atrial fibrillation, ischemic core volume, and poor collateral vessel status (OR = 1.165; 95% CI = 1.002-1.356; P = 0.047). No significant prediction was found for the net water uptake of the core in the multivariate regression model.

Conclusion

Net water uptake measured acutely within the ischemic penumbra could predict severe cerebral edema at 24-48 h.

Artificial Intelligence for Clinical Decision Support in Acute Ischemic Stroke: A Systematic Review

BACKGROUND

Established randomized trial-based parameters for acute ischemic stroke group patients into generic treatment groups, leading to attempts using various artificial intelligence (AI) methods to directly correlate patient characteristics to outcomes and thereby provide decision support to stroke clinicians. We review AI-based clinical decision support systems in the development stage, specifically regarding methodological robustness and constraints for clinical implementation.

METHODS

Our systematic review included full-text English language publications proposing a clinical decision support system using AI techniques for direct decision support in acute ischemic stroke cases in adult patients. We (1) describe data and outcomes used in those systems, (2) estimate the systems' benefits compared with traditional stroke diagnosis and treatment, and (3) reported concordance with reporting standards for AI in healthcare.

RESULTS

One hundred twenty-one studies met our inclusion criteria. Sixty-five were included for full extraction. In our sample, utilized data sources, methods, and reporting practices were highly heterogeneous.

CONCLUSIONS

Our results suggest significant validity threats, dissonance in reporting practices, and challenges to clinical translation. We outline practical recommendations for the successful implementation of AI research in acute ischemic stroke treatment and diagnosis.

Quality assessment of stroke radiomics studies: Promoting clinical application

PURPOSE

To evaluate the quality of radiomics studies on stroke using a radiomics quality score (RQS), Minimum Information for Medial AI reporting (MINIMAR) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) to promote clinical application.

METHODS

PubMed MEDLINE and Embase were searched to identify radiomics studies on stroke. Of 464 articles, 52 relevant original research articles were included. The RQS, MINIMAR and TRIPOD were scored to evaluate the quality of the studies by neuroradiologists.

RESULTS

Only four studies (7.7 %) performed external validation. The mean RQS was 3.2 of 36 (8.9 %), and the basic adherence rate was 24.9 %. The adherence rate was low for conducting phantom study (1.9 %), stating comparison to 'gold standard' (1.9 %), offering potential clinical utility (13.5 %) and performing cost-effectiveness analysis (1.9 %). None of the studies performed a test-retest, stated biologic correlation, conducted prospective studies, or opened codes and data to the public, resulting in low RQS. The total MINIMAR adherence rate was 47.4 %. The overall adherence rate for TRIPOD was 54.6 %, with low scores for reporting the title (2.0 %), key elements of the study setting (6.1 %), and explaining the sample size (2.0 %).

CONCLUSIONS

The overall radiomics reporting quality and reporting of published radiomics studies on stoke was suboptimal. More thorough validation and open data are needed to increase clinical applicability of radiomics studies.

Association Between Net Water Uptake and Functional Outcome in Patients With Low ASPECTS Brain Lesions: Results From the I-LAST Study

BACKGROUND AND OBJECTIVES

The effect of mechanical thrombectomy (MT) on functional outcome in patients with ischemic stroke with low ASPECTS is still uncertain. ASPECTS rating is based on the presence of ischemic hypoattenuation relative to normal; however, the degree of hypoattenuation, which directly reflects net uptake of water, is currently not considered an imaging biomarker in stroke triage. We hypothesized that the effect of thrombectomy on functional outcome in low ASPECTS patients depends on early lesion water uptake.

METHODS

For this multicenter observational study, patients with anterior circulation stroke with ASPECTS ≤5 were consecutively analyzed. Net water uptake (NWU) was assessed as a quantitative imaging biomarker in admission CT. The primary end point was the rate of favorable functional outcome defined as modified Rankin Scale score 0-3 at day 90. The effect of recanalization on functional outcome was analyzed according to the degree of NWU within the early infarct lesion.

RESULTS

A total of 254 patients were included, of which 148 (58%) underwent MT. The median ASPECTS was 4 (interquartile range [IQR] 3-5), and the median NWU was 11.4% (IQR 8.9%-15.1%). The rate of favorable outcome was 27.6% in patients with low NWU (<11.4%) vs 6.3% in patients with high NWU (≥11.4%; p < 0.0001). In multivariable logistic regression analysis, NWU was an independent predictor of outcome, whereas vessel recanalization (modified thrombolysis in cerebral infarction ≥2b) was only significantly associated with better outcomes if NWU was lower than 12.6%. In inverse-probability weighting analysis, recanalization was associated with 20.7% (p = 0.01) increase in favorable outcome in patients with low NWU compared with 9.1% (p = 0.06) in patients with high NWU.

DISCUSSION

Early NWU was independently associated with clinical outcome and might serve as an indicator of futile MT in low ASPECTS patients. NWU could be tested as a tool to select low ASPECTS patients for MT.

TRIAL REGISTRATION INFORMATION

The study is registered within the ClinicalTrials.gov Protocol Registration and Results System (NCT04862507).

Introduction to radiomics for a clinical audience

Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.

ASPECTS-based net water uptake predicts poor reperfusion and poor clinical outcomes in patients with ischemic stroke

OBJECTIVE

To investigate the value of automated Alberta Stroke Program Early CT Score (ASPECTS)-based net water uptake (NWU) to predict tissue-level reperfusion status and 90-day functional outcomes in acute ischemic stroke (AIS) patients after reperfusion therapy.

METHODS

One hundred and twelve patients with AIS who received reperfusion therapy were enrolled. ASPECTS-NWU was calculated from admission CT (NWU_admission) and follow-up CT (NWU_FCT), and the difference (ΔNWU) was calculated. Tissue-level reperfusion status was evaluated via follow-up arterial spin labeling imaging. The relationship between ASPECTS-NWU and tissue-level reperfusion was evaluated. Predictors of 90-day unfavorable outcomes (modified Rankin Scale score > 2) were assessed by multivariate logistic regression analysis and receiver operating characteristic (ROC) curves.

RESULTS

Poor reperfusion was observed in 40 patients (35.7%) after therapy. Those patients had significantly elevated NWU_FCT (median, 14.15% vs. 8.08%, p = 0.018) and higher ΔNWU (median, 4.12% vs. -2.03%, p < 0.001), compared to patients with good reperfusion. High ΔNWU was a significant marker of poor reperfusion despite successful recanalization. National Institutes of Health Stroke Scale score at admission (odds ratio [OR], 1.11; 95% confidence interval [CI] 1.03-1.20, p = 0.007) and ΔNWU (OR, 1.07; 95% CI 1.02-1.13, p = 0.008) were independently associated with unfavorable outcomes. An outcome prediction model including both parameters yields an area under the curve of 0.762 (sensitivity 70.3%, specificity, 84.2%).

CONCLUSIONS

Elevated NWU_FCT and higher ΔNWU were associated with poor tissue-level reperfusion after therapy. Higher ΔNWU was an independent predictor of poor reperfusion and unfavorable neurological outcomes despite successful recanalization.

KEY POINTS

• ASPECTS-NWU may provide pathophysiological information about tissue-level reperfusion status and offer prognostic benefits for patients with AIS after reperfusion therapy. • Elevated NWU_FCT and higher ΔNWU were correlated with poor tissue-level reperfusion after therapy. • A higher ΔNWU is an independent predictor of poor reperfusion and 90-day unfavorable outcomes despite successful recanalization.

Automated Measurement of Net Water Uptake From Baseline and Follow-Up CTs in Patients With Large Vessel Occlusion Stroke

Quantifying the extent and evolution of cerebral edema developing after stroke is an important but challenging goal. Lesional net water uptake (NWU) is a promising CT-based biomarker of edema, but its measurement requires manually delineating infarcted tissue and mirrored regions in the contralateral hemisphere. We implement an imaging pipeline capable of automatically segmenting the infarct region and calculating NWU from both baseline and follow-up CTs of large-vessel occlusion (LVO) patients. Infarct core is extracted from CT perfusion images using a deconvolution algorithm while infarcts on follow-up CTs were segmented from non-contrast CT (NCCT) using a deep-learning algorithm. These infarct masks were flipped along the brain midline to generate mirrored regions in the contralateral hemisphere of NCCT; NWU was calculated as one minus the ratio of densities between regions, removing voxels segmented as CSF and with HU outside thresholds of 20-80 (normal hemisphere and baseline CT) and 0-40 (infarct region on follow-up). Automated results were compared with those obtained using manually-drawn infarcts and an ASPECTS region-of-interest based method that samples densities within the infarct and normal hemisphere, using intraclass correlation coefficient (ρ). This was tested on serial CTs from 55 patients with anterior circulation LVO (including 66 follow-up CTs). Baseline NWU using automated core was 4.3% (IQR 2.6-7.3) and correlated with manual measurement (ρ = 0.80, p < 0.0001) and ASPECTS (r = -0.60, p = 0.0001). Automatically segmented infarct volumes (median 110-ml) correlated to manually-drawn volumes (ρ = 0.96, p < 0.0001) with median Dice similarity coefficient of 0.83 (IQR 0.72-0.90). Automated NWU was 24.6% (IQR 20-27) and highly correlated to NWU from manually-drawn infarcts (ρ = 0.98) and the sampling-based method (ρ = 0.68, both p < 0.0001). We conclude that this automated imaging pipeline is able to accurately quantify region of infarction and NWU from serial CTs and could be leveraged to study the evolution and impact of edema in large cohorts of stroke patients.

Evaluation and Prediction of Post-stroke Cerebral Edema Based on Neuroimaging

Cerebral edema is a common complication of acute ischemic stroke that leads to poorer functional outcomes and substantially increases the mortality rate. Given that its negative effects can be reduced by more intensive monitoring and evidence-based interventions, the early identification of patients with a high risk of severe edema is crucial. Neuroimaging is essential for the assessment and prediction of edema. Simple markers, such as midline shift and hypodensity volume on computed tomography, have been used to evaluate edema in clinical trials; however, advanced techniques can be applied to examine the underlying mechanisms. In this study, we aimed to review current imaging tools in the assessment and prediction of cerebral edema to provide guidance for using these methods in clinical practice.

Deep symmetric three-dimensional convolutional neural networks for identifying acute ischemic stroke via diffusion-weighted images

BACKGROUND

Acute ischemic stroke (AIS) results in high morbidity, disability, and mortality. Early and automatic diagnosis of AIS can help clinicians administer the appropriate interventions.

OBJECTIVE

To develop a deep symmetric 3D convolutional neural network (DeepSym-3D-CNN) for automated AIS diagnosis via diffusion-weighted imaging (DWI) images.

METHODS

This study includes 190 study subjects (97 AIS and 93 Non-AIS) by collecting both DWI and Apparent Diffusion Coefficient (ADC) images. 3D DWI brain images are split into left and right hemispheres and input into two paths. A map with 125×253×14×12 features is extracted by each path of Inception Modules. After the features computed from two paths are subtracted through L-2 normalization, four multi-scale convolution layers produce the final predation. Three comparative models using DWI images including MedicalNet with transfer learning, Simple DeepSym-3D-CNN (each 3D Inception Module is replaced by a simple 3D-CNN layer), and L-1 DeepSym-3D-CNN (L-2 normalization is replaced by L-1 normalization) are constructed. Moreover, using ADC images and the combination of DWI and ADC images as inputs, the performance of DeepSym-3D-CNN is also investigated. Performance levels of all three models are evaluated by 5-fold cross-validation and the values of area under ROC curve (AUC) are compared by DeLong's test.

RESULTS

DeepSym-3D-CNN achieves an accuracy of 0.850 and an AUC of 0.864. DeLong's test of AUC values demonstrates that DeepSym-3D-CNN significantly outperforms other comparative models (p < 0.05). The highlighted regions in the feature maps of DeepSym-3D-CNN spatially match with AIS lesions. Meanwhile, DeepSym-3D-CNN using DWI images presents the significant higher AUC than that either using ADC images or using DWI-ADC images based on DeLong's test (p < 0.05).

CONCLUSIONS

DeepSym-3D-CNN is a potential method for automatically identifying AIS via DWI images and can be extended to other diseases with asymmetric lesions.