The Los Angeles motor scale (LAMS) and ASPECTS score are independently associated with DSA ASITN collateral score.

BACKGROUND: Mechanical thrombectomy (MT) is the treatment standard in eligible patients with acute ischemic stroke (AIS) secondary to large vessel occlusions (LVO). Studies have shown that good collateral status is a strong predictor of MT efficacy, thus making collateral status important to quickly assess. The Los Angeles Motor Scale is a clinically validated tool for identifying LVO in the field. The aim of this study is to investigate whether admission LAMS score is also associated with the American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score on digital subtraction angiography (DSA). METHODS: We conducted a retrospective multicenter cohort study of consecutive patients presenting with AIS caused by LVO from 9/1/2017 to 10/1/2023 with diagnostically adequate DSA imaging. Demographic, clinical, and imaging data was collected through manual chart review. Both univariate and multivariate analysis were applied to assess associations. A p-value <0.05 was considered significant. RESULTS: A total of 308 patients (median age: 68, IQR: 57.5-77) were included in the study. On multivariate logistic regression analysis, we found that lower admission LAMS score (adjusted OR: 0.82, 95% CI: 0.68-0.98, p < 0.05) and higher ASPECTS score (adjusted OR: 1.21, 95% CI: 1.02-1.42, p < 0.05) were independently associated with good DSA ASITN collateral score of 3-4. CONCLUSIONS: Admission LAMS and ASPECTS score are both independently associated with DSA ASITN collateral score. This demonstrates the capability of LAMS to act as a surrogate marker of CS in the field.

Deep Learning-Based ASPECTS Algorithm Enhances Reader Performance and Reduces Interpretation Time.

BACKGROUND AND PURPOSE: ASPECTS is a long-standing and well documented selection criteria for acute ischemic stroke treatment, however, the interpretation of ASPECTS is a challenging and time-consuming task for physicians with significant interobserver variabilities. We conducted a multi-reader, multi-case study in which readers assessed ASPECTS without and with the support of a deep learning (DL)-based algorithm in order to analyze the impact of the software on clinicians' performance and interpretation time. MATERIALS AND METHODS: A total of 200 NCCT scans from 5 clinical sites (27 scanner models, 4 different vendors) were retrospectively collected. Reference standard was established through the consensus of three expert neuroradiologists who had access to baseline CTA and CTP data. Subsequently, eight additional clinicians (four typical ASPECTS reader and four senior neuroradiologists) analyzed the NCCT scans without and with the assistance of CINA-ASPECTS (Avicenna.AI, La Ciotat, France), a DLbased FDA-cleared and CE-marked algorithm designed to automatically compute ASPECTS. Differences were evaluated in both performance and interpretation time between the assisted and unassisted assessments. RESULTS: With software aid, readers demonstrated increased region-based accuracy from 72.4% to 76.5% (p<0.05), and increased ROC AUC from 0.749 to 0.788 (p<0.05). Notably, all readers exhibited an improved ROC AUC when utilizing the software. Moreover, use of the algorithm improved the score-based inter-observer reliability and correlation coefficient of ASPECTS evaluation by 0.222 and 0.087 (p<0.0001), respectively. Additionally, the readers' mean time spent analyzing a case was significantly reduced by 6% (p<0.05) when aided by the algorithm. CONCLUSIONS: With the assistance of the algorithm, readers' analyses were not only more accurate but also faster. Additionally, the overall ASPECTS evaluation exhibited greater consistency, less variabilities and higher precision compared to the reference standard. This novel tool has the potential to enhance patient selection for appropriate treatment by enabling physicians to deliver accurate and timely diagnosis of acute ischemic stroke. ABBREVIATIONS: ASPECTS = Alberta Stroke Program Early Computed Tomography Score; DL = Deep Learning; EIC = Early Ischemic Changes; ICC = Intraclass Correlation Coefficient; IS = Ischemic Stroke; ROC AUC = Receiver Operating Characteristics Area Under the Curve.

Perfusion imaging predicts short-term clinical outcome in isolated posterior cerebral artery occlusion stroke.

BACKGROUND AND PURPOSE: Ischemic strokes due to isolated posterior cerebral artery (PCA) occlusions represent 5% of all strokes but have significant impacts on patients' quality of life, primarily due to visual deficits and thalamic involvement. Current guidelines for acute PCA occlusion management are sparse, and the prognostic value of perfusion imaging parameters remains underexplored. METHODS: We conducted a retrospective analysis of 32 patients with isolated PCA occlusions treated at Johns Hopkins Medical Institutions between January 2017 and March 2023. Patients underwent pretreatment perfusion imaging, with perfusion parameters analyzed using RAPID software. The primary outcome was short-term clinical outcome as measured by the National Institutes of Health Stroke Scale (NIHSS) at discharge. RESULTS: The median age of the cohort was 70 years, with 34% female and 66% male. Significant correlations were found between NIHSS at discharge and various perfusion parameters, including time-to-maximum (Tmax) >6 seconds (ρ = .55, p = .004), Tmax >8 seconds (ρ = .59, p = .002), Tmax >10 seconds (ρ = .6, p = .001), mismatch volume (ρ = .51, p = .008), and cerebral blood volume (CBV) < 34% (ρ = .59, p = .002). CONCLUSIONS: Tmax and CBV volumes significantly correlated with discharge NIHSS with marginal superiority of Tmax >10 seconds and CBV <42% volumes. These findings suggest that CT and MR perfusion imaging can play a crucial role in the acute management of PCA strokes, though larger, standardized studies are needed to validate these results and refine imaging thresholds specific to posterior circulation infarcts.

Decreased Quantitative Cerebral Blood Volume Is Associated With Poor Outcomes in Large Core Patients.

BACKGROUND: Recent large core trials have highlighted the effectiveness of mechanical thrombectomy (MT) in acute ischemic stroke with large vessel occlusion. Variable perfusion-imaging thresholds and poor Alberta Stroke Program Early Computed Tomography Score reliability underline the need for more standardized, quantitative ischemia measures for MT patient selection. We aimed to identify the computed tomography perfusion parameter most strongly associated with poor outcomes in patients with acute ischemic stroke-large vessel occlusion with significant ischemic cores. METHODS: In this study from 2 comprehensive stroke centers from 2 comprehensive stroke centers within the Johns Hopkins Medical Enterprise (Johns Hopkins Hospita-East Baltimore and Bayview Medical Campus) from July 29, 2019 to January 29, 2023 in a continuously maintained database, we included patients with acute ischemic stroke-large vessel occlusion with ischemic core volumes defined as relative cerebral blood flow <30% and ≥50 mL on computed tomography perfusion or Alberta Stroke Program Early Computed Tomography Score <6. We used receiver operating characteristics to find the optimal cutoff for parameters like cerebral blood volume (CBV) <34%, 38%, 42%, and relative cerebral blood flow >20%, 30%, 34%, 38%, and time-to-maximum >4, 6, 8, and 10 seconds. The primary outcome was unfavorable outcomes (90-day modified Rankin Scale score 4-6). Multivariable models were adjusted for age, sex, diabetes, baseline National Institutes of Health Stroke Scale, intravenous thrombolysis, and MT. RESULTS: We identified 59 patients with large ischemic cores. A receiver operating characteristic curve analysis showed that CBV<42% ≥68 mL is associated with unfavorable outcomes (90-day modified Rankin Scale score 4-6) with an area under the curve of 0.90 (95% CI, 0.82-0.99) in the total and MT-only cohorts. Dichotomizing at this CBV threshold, patients in the ≥68 mL group exhibited significantly higher relative cerebral blood flow, time-to-maximum >8 and 10 seconds volumes, higher CBV volumes, higher HIR, and lower CBV index. The multivariable model incorporating CBV<42% ≥68 mL predicted poor outcomes robustly in both cohorts (area under the curve for MT-only subgroup was 0.87 [95% CI, 0.75-1.00]). CONCLUSIONS: CBV<42% ≥68 mL most effectively forecasts poor outcomes in patients with large-core stroke, confirming its value alongside other parameters like time-to-maximum in managing acute ischemic stroke-large vessel occlusion.

Simplifying venous outflow: Prolonged venous transit as a novel qualitative marker correlating with acute stroke outcomes.

BACKGROUND: Prolonged venous transit (PVT), defined as presence of time-to-maximum ≥ 10 s within the superior sagittal sinus (SSS) and/or torcula, is a novel, qualitatively assessed computed tomography perfusion surrogate parameter of venous outflow with potential utility in pretreatment acute ischemic stroke imaging for neuroprognostication. We aim to characterize the correlation between PVT and neurological functional outcomes in thrombectomy-treated patients. METHODS: A prospectively-collected database of large vessel occlusion acute ischemic stroke patients treated with thrombectomy was retrospectively analyzed. Spearman's rank correlation coefficient and point-biserial correlations were performed between PVT status (i.e., no region, either SSS or torcula, or both), 90-day modified Rankin score (mRS), mortality (mRS 6), and poor functional outcome (mRS 4-6 vs 0-3). RESULTS: Of 128 patients, correlation between PVT and 90-day mRS (ρ = 0.35, p < 0.0001), mortality (r = 0.26, p = 0.002), and poor functional outcome (r = 0.27, p = 0.002) were significant. CONCLUSION: There is a modest, significant correlation between PVT and severity of neurological functional outcome. Consequently, PVT is an easily-ascertained, qualitative metric that may be useful as an adjunct for anticipating a patient's clinical course. Future analyses will determine the significance of incorporating PVT in clinical decision-making.

A Deep Learning Approach to Predict Recanalization First-Pass Effect following Mechanical Thrombectomy in Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: Following endovascular thrombectomy in patients with large-vessel occlusion stroke, successful recanalization from 1 attempt, known as the first-pass effect, has correlated favorably with long-term outcomes. Pretreatment imaging may contain information that can be used to predict the first-pass effect. Recently, applications of machine learning models have shown promising results in predicting recanalization outcomes, albeit requiring manual segmentation. In this study, we sought to construct completely automated methods using deep learning to predict the first-pass effect from pretreatment CT and MR imaging. MATERIALS AND METHODS: Our models were developed and evaluated using a cohort of 326 patients who underwent endovascular thrombectomy at UCLA Ronald Reagan Medical Center from 2014 to 2021. We designed a hybrid transformer model with nonlocal and cross-attention modules to predict the first-pass effect on MR imaging and CT series. RESULTS: The proposed method achieved a mean 0.8506 (SD, 0.0712) for cross-validation receiver operating characteristic area under the curve (ROC-AUC) on MR imaging and 0.8719 (SD, 0.0831) for cross-validation ROC-AUC on CT. When evaluated on the prospective test sets, our proposed model achieved a mean ROC-AUC of 0.7967 (SD, 0.0335) with a mean sensitivity of 0.7286 (SD, 0.1849) and specificity of 0.8462 (SD, 0.1216) for MR imaging and a mean ROC-AUC of 0.8051 (SD, 0.0377) with a mean sensitivity of 0.8615 (SD, 0.1131) and specificity 0.7500 (SD, 0.1054) for CT, respectively, representing the first classification of the first-pass effect from MR imaging alone and the first automated first-pass effect classification method in CT. CONCLUSIONS: Results illustrate that both nonperfusion MR imaging and CT from admission contain signals that can predict a successful first-pass effect following endovascular thrombectomy using our deep learning methods without requiring time-intensive manual segmentation.

The Los Angeles motor scale (LAMS) is independently associated with CT perfusion collateral status markers.

BACKGROUND AND AIM: The Los Angeles Motor Scale (LAMS) is an objective tool that has been used to rapidly assess and predict the presence of large vessel occlusion (LVO) in the pre-hospital setting successfully in several studies. However, studies assessing the relationship between LAMS score and CT perfusion collateral status (CS) markers such as cerebral blood volume (CBV) index, and hypoperfusion intensity ratio (HIR) are sparse. Our study therefore aims to assess the association of admission LAMS score with established CTP CS markers CBV Index and HIR in AIS-LVO cases. MATERIALS AND METHODS: In this prospectively collected, retrospectively reviewed analysis, inclusion criteria were as follows: a) CT angiography (CTA) confirmed anterior circulation LVO from 9/1/2017 to 10/01/2023, and b) diagnostic CT perfusion (CTP). Logistic regression analysis was performed to assess the relationship between admission LAMS with CTP CS markers HIR and CBV Index. p ≤ 0.05 was considered significant. RESULTS: In total, 285 consecutive patients (median age = 69 years; 56 % female) met our inclusion criteria. Multivariable logistic regression analysis adjusting for sex, age, ASPECTS, tPA, premorbid mRS, admission NIH stroke scale, prior history of TIA, stroke, atrial fibrillation, diabetes mellitus, hyperlipidemia, coronary artery disease and hypertension, admission LAMS was found to be independently associated with CBV Index (adjusted OR:0.82, p < 0.01), and HIR (adjusted OR:0.59, p < 0.05). CONCLUSION: LAMS is independently associated with CTP CS markers, CBV index and HIR. This finding suggests that LAMS may also provide an indirect estimate of CS.

Lower admission stroke severity is associated with good collateral status in distal medium vessel occlusion stroke.

BACKGROUND AND PURPOSE: Distal medium vessel occlusions (DMVOs) are a significant contributor to acute ischemic stroke (AIS), with collateral status (CS) playing a pivotal role in modulating ischemic damage progression. We aimed to explore baseline characteristics associated with CS in AIS-DMVO. METHODS: This retrospective analysis of a prospectively collected database enrolled 130 AIS-DMVO patients from two comprehensive stroke centers. Baseline characteristics, including patient demographics, admission National Institutes of Health Stroke Scale (NIHSS) score, admission Los Angeles Motor Scale (LAMS) score, and co-morbidities, including hypertension, hyperlipidemia, diabetes, coronary artery disease, atrial fibrillation, and history of transient ischemic attack or stroke, were collected. The analysis was dichotomized to good CS, reflected by hypoperfusion index ratio (HIR) <.3, versus poor CS, reflected by HIR ≥.3. RESULTS: Good CS was observed in 34% of the patients. As to the occluded location, 43.8% occurred in proximal M2, 16.9% in mid M2, 35.4% in more distal middle cerebral artery, and 3.8% in distal anterior cerebral artery. In multivariate logistic analysis, a lower NIHSS score and a lower LAMS score were both independently associated with a good CS (odds ratio [OR]: 0.88, 95% confidence interval [CI]: 0.82-0.95, p < .001 and OR: 0.77, 95% CI: 0.62-0.96, p = .018, respectively). Patients with poor CS were more likely to manifest as moderate to severe stroke (29.1% vs. 4.5%, p < .001), while patients with good CS had a significantly higher chance of having a minor stroke clinically (40.9% vs. 12.8%, p < .001). CONCLUSIONS: CS remains an important determinant in the severity of AIS-DMVO. Collateral enhancement strategies may be a worthwhile pursuit in AIS-DMVO patients with more severe initial stroke presentation, which can be swiftly identified by the concise LAMS and serves as a proxy for underlying poor CS.

Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.

Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. Methods: The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). Results: An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett's post-hoc tests were performed after ANOVA, and a significance level of p < 0.05 was defined. There was no significant effect of kernel (p = 0.91), a significant effect of slice thickness (p < 0.01) and no significant interaction between these factors (p = 0.91). Post-hoc tests indicated no significant difference between ASPECTS estimated in the reference and any non-reference conditions. There was a significant effect of kernel (p < 0.01) and slice thickness (p < 0.01) on hypodense volume, however there was no significant interaction between these factors (p = 0.79). Post-hoc tests indicated significantly different hypodense volume measurements for H10s/1.5mm (p = 0.03), H40s/1.5mm (p < 0.01), H70h/5mm (p < 0.01). No significant difference was found in hypodense volume measured in the H10s/5mm condition (p = 0.96). Conclusion: Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

CT Perfusion Derived rCBV < 42% Lesion Volume Is Independently Associated with Followup FLAIR Infarct Volume in Anterior Circulation Large Vessel Occlusion.

Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearman's rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = -0.214, p < 0.01), mTICI (beta = -0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

Prolonged venous transit on perfusion imaging is associated with higher odds of mortality in successfully reperfused patients with large vessel occlusion stroke.

BACKGROUND: Poor venous outflow (VO) profiles are associated with unfavorable outcomes in patients with acute ischemic stroke caused by large vessel occlusion (AIS-LVO), despite achieving successful reperfusion. The objective of this study is to assess the association between mortality and prolonged venous transit (PVT), a novel visual qualitative VO marker on CT perfusion (CTP) time to maximum (Tmax) maps. METHODS: We performed a retrospective analysis of prospectively collected data from consecutive adult patients with AIS-LVO with successful reperfusion (modified Thrombolysis in Cerebral Infarction 2b/2c/3). PVT+ was defined as Tmax ≥10 s timing on CTP Tmax maps in at least one of the following: superior sagittal sinus (proximal venous drainage) and/or torcula (deep venous drainage). PVT- was defined as lacking this in both regions. The primary outcome was mortality at 90 days. In a 1:1 propensity score-matched cohort, regressions were performed to determine the effect of PVT on 90-day mortality. RESULTS: In 127 patients of median (IQR) age 71 (64-81) years, mortality occurred in a significantly greater proportion of PVT+ patients than PVT- patients (32.5% vs 12.6%, P=0.01). This significant difference persisted after matching (P=0.03). PVT+ was associated with a significantly increased likelihood of 90-day mortality (OR 1.22 (95% CI 1.02 to 1.46), P=0.03) in the matched cohort. CONCLUSIONS: PVT+ was significantly associated with 90-day mortality despite successful reperfusion therapy in patients with AIS-LVO. PVT is a simple VO profile marker with potential as an adjunctive metric during acute evaluation of AIS-LVO patients. Future studies will expand our understanding of using PVT in the evaluation of patients with AIS-LVO.

Follow-up infarct volume on fluid attenuated inversion recovery (FLAIR) imaging in distal medium vessel occlusions: the role of cerebral blood volume index.

BACKGROUND: Distal medium vessel occlusions (DMVOs) contribute substantially to the incidence of acute ischemic strokes (AIS) and pose distinct challenges in clinical management and prognosis. Neuroimaging techniques, such as Fluid Attenuation Inversion Recovery (FLAIR) imaging and cerebral blood volume (CBV) index derived from perfusion imaging, have significantly improved our ability to assess the impact of strokes and predict their outcomes. The primary objective of this study was to investigate relationship between follow-up infarct volume (FIV) as assessed by FLAIR imaging in patients with DMVOs. METHODS: This prospectively collected, retrospective reviewed cohort study included patients from two comprehensive stroke centers within the Johns Hopkins Medical Enterprise, spanning August 2018-October 2022. The cohort consisted of adults with AIS attributable to DMVO. Detailed imaging analyses were conducted, encompassing non-contrast CT, CT angiography (CTA), CT perfusion (CTP), and FLAIR imaging. Univariable and multivariable linear regression models were employed to assess the association between different factors and FIV. RESULTS: The study included 79 patients with DMVO stroke with a median age of 69 years (IQR, 62-77 years), and 57% (n = 45) were female. There was a negative correlation between the CBV index and FIV in a univariable linear regression analysis (Beta = - 16; 95% CI, - 23 to - 8.3; p < 0.001) and a multivariable linear regression model (Beta = - 9.1 per 0.1 change; 95% CI, - 15 to - 2.7; p = 0.006). Diabetes was independently associated with larger FIV (Beta = 46; 95% CI, 16 to 75; p = 0.003). Additionally, a higher baseline ASPECTS was associated with lower FIV (Beta = - 30; 95% CI, - 41 to - 20; p < 0.001). CONCLUSION: Our findings underscore the CBV index as an independent association with FIV in DMVOs, which highlights the critical role of collateral circulation in determining stroke outcomes in this patient population. In addition, our study confirms a negative association of ASPECTS with FLAIR FIV and identifies diabetes as independent factor associated with larger FIV. These insights pave the way for further large-scale, prospective studies to corroborate these findings, thereby refining the strategies for stroke prognostication and management.

The Relative Cerebral Blood Volume (rCBV) < 42% Is Independently Associated with Collateral Status in Anterior Circulation Large Vessel Occlusion.

Background: The pretreatment CT perfusion (CTP) marker the relative cerebral blood volume (rCBV) < 42% lesion volume has recently been shown to predict 90-day functional outcomes; however, studies assessing correlations of the rCBV < 42% lesion volume with other outcomes remain sparse. Here, we aim to assess the relationship between the rCBV < 42% lesion volume and the reference standard digital subtraction angiography (DSA)-derived American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN) collateral score, hereby referred as the DSA CS. Methods: In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 1 September 2017 and 1 October 2023. Group differences were assessed using the Student's t test, Mann-Whitney U test and Chi-Square test. Spearman's rank correlation and logistic regression analyses were used to assess associations between rCBV < 42% and DSA CS. Results: In total, 222 patients (median age: 69 years, 56.3% female) met our inclusion criteria. In the multivariable logistic regression analysis, taking into account age, sex, race, hypertension, hyperlipidemia, diabetes, atrial fibrillation, prior stroke or transient ischemic attack, the admission National Institute of Health stroke scale, the premorbid modified Rankin score, the Alberta stroke program early CT score (ASPECTS), and segment occlusion, the rCBV < 42% lesion volume (adjusted OR: 0.98, p < 0.05) was independently associated with the DSA CS. Conclusion: The rCBV < 42% lesion volume is independently associated with the DSA CS.

Intracranial aneurysm wall displacement depicted by amplified Flow predicts growth.

BACKGROUND: Abnormal intracranial aneurysm (IA) wall motion has been associated with IA growth and rupture. Recently, a new image processing algorithm called amplified Flow (aFlow) has been used to successfully track IA wall motion by combining the amplification of cine and four-dimensional (4D) Flow MRI. We sought to apply aFlow to assess wall motion as a potential marker of IA growth in a paired-wise analysis of patients with growing versus stable aneurysms. METHODS: In this retrospective case-control study, 10 patients with growing IAs and a matched cohort of 10 patients with stable IAs who had baseline 4D Flow MRI were included. The aFlow was used to amplify and extract IA wall displacements from 4D Flow MRI. The associations of aFlow parameters with commonly used risk factors and morphometric features were assessed using paired-wise univariate and multivariate analyses. RESULTS: aFlow quantitative results showed significantly (P=0.035) higher wall motion displacement depicted by mean±SD 90th% values of 2.34±0.72 in growing IAs versus 1.39±0.58 in stable IAs with an area under the curve of 0.85. There was also significantly (P<0.05) higher variability of wall deformation across IA geometry in growing versus stable IAs depicted by the dispersion variables including 121-150% larger standard deviation ([Formula: see text]) and 128-161% wider interquartile range [Formula: see text]. CONCLUSIONS: aFlow-derived quantitative assessment of IA wall motion showed greater wall motion and higher variability of wall deformation in growing versus stable IAs.

Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.

BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.

AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical.

BACKGROUND AND PURPOSE: The review of clinical reports is an essential part of monitoring disease progression. Synthesizing multiple imaging reports is also important for clinical decisions. It is critical to aggregate information quickly and accurately. Machine learning natural language processing (NLP) models hold promise to address an unmet need for report summarization. MATERIALS AND METHODS: We evaluated NLP methods to summarize longitudinal aneurysm reports. A total of 137 clinical reports and 100 PubMed case reports were used in this study. Models were 1) compared against expert-generated summary using longitudinal imaging notes collected in our institute and 2) compared using publicly accessible PubMed case reports. Five AI models were used to summarize the clinical reports, and a sixth model, the online GPT3davinci NLP large language model (LLM), was added for the summarization of PubMed case reports. We assessed the summary quality through comparison with expert summaries using quantitative metrics and quality reviews by experts. RESULTS: In clinical summarization, BARTcnn had the best performance (BERTscore = 0.8371), followed by LongT5Booksum and LEDlegal. In the analysis using PubMed case reports, GPT3davinci demonstrated the best performance, followed by models BARTcnn and then LEDbooksum (BERTscore = 0.894, 0.872, and 0.867, respectively). CONCLUSIONS: AI NLP summarization models demonstrated great potential in summarizing longitudinal aneurysm reports, though none yet reached the level of quality for clinical usage. We found the online GPT LLM outperformed the others; however, the BARTcnn model is potentially more useful because it can be implemented on-site. Future work to improve summarization, address other types of neuroimaging reports, and develop structured reports may allow NLP models to ease clinical workflow.

Pretreatment CT perfusion collateral parameters correlate with penumbra salvage in middle cerebral artery occlusion.

BACKGROUND AND PURPOSE: Acute ischemic stroke due to large vessel occlusion (AIS-LVO) is a major cause of functional dependence. Collateral status (CS) is an important determinant of functional outcomes. Pretreatment CT perfusion (CTP) parameters serve as reliable surrogates of CS. Penumbra Salvage Index (PSI) is another parameter predictive of functional outcomes in AIS-LVO. The aim of this study is to assess the relationship of pretreatment CTP parameters with PSI. METHODS: In this prospectively collected, retrospectively reviewed multicenter analysis, inclusion criteria were as follows: (1) CT angiography confirmed middle cerebral artery (MCA) M1-segment and proximal M2-segment occlusion from 9/1/2017 to 9/22/2022; (2) diagnostic CTP; and (3) available diagnostic Magnetic resonance Imaging (MRI) diffusion-weighted images. Pearson correlation analysis was performed to assess the association between cerebral blood volume (CBV) index and hypoperfusion intensity ratio (HIR) with PSI. p value ≤.05 was considered statistically significant. RESULTS: In total, 131 patients (n = 86, M1 and n = 45, proximal M2 occlusion) met our inclusion criteria. CBV index showed a modest positive correlation with PSI (r = 0.34, p<.001) in patients with proximal MCA occlusion. Similar trends were noted in subgroup analysis of patients with M1 occlusion, and proximal M2 occlusion. Whereas, HIR did not have a strong trend or correlation with PSI. CONCLUSION: CBV index correlates with PSI, whereas HIR does not. Future studies are needed to expand our understanding of the adjunct role of CBV index with other similar pretreatment CTP-based markers in clinical evaluation and decision-making in patients with MCA occlusion.

Redefining CT perfusion-based ischemic core estimates for the ghost core in early time window stroke.

BACKGROUND AND PURPOSE: In large vessel occlusion (LVO) stroke patients, relative cerebral blood flow (rCBF)<30% volume thresholds are commonly used in treatment decisions. In the early time window, nearly infarcted but salvageable tissue volumes may lead to pretreatment overestimates of infarct volume, and thus potentially exclude patients who may otherwise benefit from intervention. Our multisite analysis aims to explore the strength of relationships between widely used pretreatment CT parameters and clinical outcomes for early window stroke patients. METHODS: Patients from two sites in a prospective registry were analyzed. Patients with LVOs, presenting within 3 hours of last known well, and who were successfully reperfused were included. Primary short-term neurological outcome was percent National Institutes of Health Stroke Scale (NIHSS) change from admission to discharge. Secondary long-term outcome was 90-day modified Rankin score. Spearman's correlations were performed. Significance was attributed to p-value ≤.05. RESULTS: Among 73 patients, median age was 66 (interquartile range 54-76) years. Among all pretreatment imaging parameters, rCBF<30%, rCBF<34%, and rCBF<38% volumes were significantly, inversely correlated with percentage NIHSS change (p<.048). No other parameters significantly correlated with outcomes. CONCLUSIONS: Our multisite analysis shows that favorable short-term neurological recovery was significantly correlated with rCBF volumes in the early time window. However, modest strength of correlations provides supportive evidence that the applicability of general ischemic core estimate thresholds in this subpopulation is limited. Our results support future larger-scale efforts to liberalize or reevaluate current rCBF parameter thresholds guiding treatment decisions for early time window stroke patients.

Pretreatment CTP Collateral Parameters Predict Good Outcomes in Successfully Recanalized Middle Cerebral Artery Distal Medium Vessel Occlusions.

BACKGROUND/PURPOSE: Distal medium vessel occlusions (DMVOs) account for a large percentage of vessel occlusions resulting in acute ischemic stroke (AIS) with disabling symptoms. We aim to assess whether pretreatment quantitative CTP collateral status (CS) parameters can serve as imaging biomarkers for good clinical outcomes prediction in successfully recanalized middle cerebral artery (MCA) DMVOs. METHODS: We performed a retrospective analysis of consecutive patients with AIS secondary to primary MCA-DMVOs who were successfully recanalized by mechanical thrombectomy (MT) defined as modified thrombolysis in cerebral infarction (mTICI) 2b, 2c, or 3. We evaluated the association between the CBV index and HIR independently with good clinical outcomes (modified Rankin score 0-2) using Spearman rank correlation, logistic regression, and ROC analyses. RESULTS: From 22 August 2018 to 18 October 2022 8/22/2018 to 10/18/2022, 60 consecutive patients met our inclusion criteria (mean age 71.2 ± 13.9 years old [mean ± SD], 35 female). The CBV index (r = -0.693, p < 0.001) and HIR (0.687, p < 0.001) strongly correlated with 90-day mRS. A CBV index ≥ 0.7 (odds ratio, OR, 2.27, range 6.94-21.23 [OR] 2.27 [6.94-21.23], p = 0.001)) and lower likelihood of prior stroke (0.13 [0.33-0.86]), p = 0.024)) were independently associated with good outcomes. The ROC analysis demonstrated good performance of the CBV index in predicting good 90-day mRS (AUC 0.73, p = 0.003) with a threshold of 0.7 for optimal sensitivity (71% [52.0-85.8%]) and specificity (76% [54.9-90.6%]). The HIR also demonstrated adequate performance in predicting good 90-day mRS (AUC 0.77, p = 0.001) with a threshold of 0.3 for optimal sensitivity (64.5% [45.4-80.8%]) and specificity (76.0% [54.9-90.6%]). CONCLUSION: A CBV index ≥ 0.7 may be independently associated with good clinical outcomes in our cohort of AIS caused by MCA-DMVOs that were successfully treated with MT. Furthermore, a HIR < 0.3 is also associated with good clinical outcomes. This is the first study of which we are aware to identify a CBV index threshold for MCA-DMVOs.

Pretreatment parameters associated with hemorrhagic transformation among successfully recanalized medium vessel occlusions.

Although pretreatment radiographic biomarkers are well established for hemorrhagic transformation (HT) following successful mechanical thrombectomy (MT) in large vessel occlusion (LVO) strokes, they are yet to be explored for medium vessel occlusion (MeVO) acute ischemic strokes. We aim to investigate pretreatment imaging biomarkers representative of collateral status, namely the hypoperfusion intensity ratio (HIR) and cerebral blood volume (CBV) index, and their association with HT in successfully recanalized MeVOs. A prospectively collected registry of acute ischemic stroke patients with MeVOs successfully recanalized with MT between 2019 and 2023 was retrospectively reviewed. A multivariate logistic regression for HT of any subtype was derived by combining significant univariate predictors into a forward stepwise regression with minimization of Akaike information criterion. Of 60 MeVO patients successfully recanalized with MT, HT occurred in 28.3% of patients. Independent factors for HT included: diabetes mellitus history (p = 0.0005), CBV index (p = 0.0071), and proximal versus distal occlusion location (p = 0.0062). A multivariate model with these factors had strong diagnostic performance for predicting HT (area under curve [AUC] 0.93, p < 0.001). Lower CBV indexes, distal occlusion location, and diabetes history are significantly associated with HT in MeVOs successfully recanalized with MT. Of note, HIR was not found to be significantly associated with HT.