Long-Term Clinical Implications of High-Risk Cardiac Computed Tomography Findings in Patients With Acute Ischemic Stroke.

BACKGROUND: Cardiac computed tomography (CT) acquired during the initial acute stroke imaging protocol (acute cardiac CT) is increasingly used to screen for cardioembolism, but information on the long-term clinical implications of its findings is lacking. METHODS AND RESULTS: We performed a prospective, single-center cohort study in which consecutive patients with ischemic stroke underwent ECG-gated acute cardiac CT and were followed up for 2 years. The primary outcome was functional outcome assessed using the modified Rankin Scale. Secondary outcomes were death and occurrence of major adverse cardiovascular events (composite of recurrent ischemic stroke, myocardial infarction, and cardiovascular death). We compared patients with and without a high-risk structural source of embolism on acute cardiac CT. Of 452 included patients, 55 (12.2%) had a high-risk source of embolism, predominantly cardiac thrombi (38 patients) and signs of endocarditis (8 patients). Follow-up at 2 years was complete for 430 (95.1%) patients. Patients with a high-risk source of embolism had a worse functional outcome (median modified Rankin Scale, 6 [IQR, 2-6] versus 2 [IQR, 1-5]; adjusted common odds ratio, 2.92 [95% CI, 1.62-5.25]), increased mortality rate (52.7% versus 23.7%; adjusted hazard ratio [HR], 3.28 [95% CI, 1.94-5.52]), and major adverse cardiovascular events (38.9% versus 17.5%; adjusted HR, 3.20 [95% CI, 1.80-5.69]). A high-risk source of embolism was not associated with recurrent ischemic stroke (11.1% versus 9.6%; adjusted HR, 1.30 [95% CI, 0.49-3.44]). CONCLUSIONS: Structural high-risk sources of embolism on acute cardiac CT in patients with ischemic stroke were associated with poor long-term functional outcome and occurrence of major adverse cardiovascular events but not with recurrent stroke.

Value of Automatically Derived Full Thrombus Characteristics: An Explorative Study of Their Associations with Outcomes in Ischemic Stroke Patients.

(1) Background: For acute ischemic strokes caused by large vessel occlusion, manually assessed thrombus volume and perviousness have been associated with treatment outcomes. However, the manual assessment of these characteristics is time-consuming and subject to inter-observer bias. Alternatively, a recently introduced fully automated deep learning-based algorithm can be used to consistently estimate full thrombus characteristics. Here, we exploratively assess the value of these novel biomarkers in terms of their association with stroke outcomes. (2) Methods: We studied two applications of automated full thrombus characterization as follows: one in a randomized trial, MR CLEAN-NO IV (n = 314), and another in a Dutch nationwide registry, MR CLEAN Registry (n = 1839). We used an automatic pipeline to determine the thrombus volume, perviousness, density, and heterogeneity. We assessed their relationship with the functional outcome defined as the modified Rankin Scale (mRS) at 90 days and two technical success measures as follows: successful final reperfusion, which is defined as an eTICI score of 2b-3, and successful first-pass reperfusion (FPS). (3) Results: Higher perviousness was significantly related to a better mRS in both MR CLEAN-NO IV and the MR CLEAN Registry. A lower thrombus volume and lower heterogeneity were only significantly related to better mRS scores in the MR CLEAN Registry. Only lower thrombus heterogeneity was significantly related to technical success; it was significantly related to a higher chance of FPS in the MR CLEAN-NO IV trial (OR = 0.55, 95% CI: 0.31-0.98) and successful reperfusion in the MR CLEAN Registry (OR = 0.88, 95% CI: 0.78-0.99). (4) Conclusions: Thrombus characteristics derived from automatic entire thrombus segmentations are significantly related to stroke outcomes.

Thrombus Imaging Characteristics to Predict Early Recanalization in Anterior Circulation Large Vessel Occlusion Stroke.

The early management of transferred patients with a large vessel occlusion (LVO) stroke could be improved by identifying patients who are likely to recanalize early. We aim to predict early recanalization based on patient clinical and thrombus imaging characteristics. We included 81 transferred anterior-circulation LVO patients with an early recanalization, defined as the resolution of the LVO or the migration to a distal location not reachable with endovascular treatment upon repeated radiological imaging. We compared their clinical and imaging characteristics with all (322) transferred patients with a persistent LVO in the MR CLEAN Registry. We measured distance from carotid terminus to thrombus (DT), thrombus length, density, and perviousness on baseline CT images. We built logistic regression models to predict early recanalization. We validated the predictive ability by computing the median area-under-the-curve (AUC) of the receiver operating characteristics curve for 100 5-fold cross-validations. The administration of intravenous thrombolysis (IVT), longer transfer times, more distal occlusions, and shorter, pervious, less dense thrombi were characteristic of early recanalization. After backward elimination, IVT administration, DT and thrombus density remained in the multivariable model, with an AUC of 0.77 (IQR 0.72-0.83). Baseline thrombus imaging characteristics are valuable in predicting early recanalization and can potentially be used to optimize repeated imaging workflow.

Impact of Intracranial Volume and Brain Volume on the Prognostic Value of Computed Tomography Perfusion Core Volume in Acute Ischemic Stroke.

BACKGROUND: Computed tomography perfusion (CTP)-estimated core volume is associated with functional outcomes in acute ischemic stroke. This relationship might differ among patients, depending on brain volume. MATERIALS AND METHODS: We retrospectively included patients from the MR CLEAN Registry. Cerebrospinal fluid (CSF) and intracranial volume (ICV) were automatically segmented on NCCT. We defined the proportion of the ICV and total brain volume (TBV) affected by the ischemic core as ICVcore and TBVcore. Associations between the core volume, ICVcore, TBVcore, and functional outcome are reported per interquartile range (IQR). We calculated the area under the curve (AUC) to assess diagnostic accuracy. RESULTS: In 200 patients, the median core volume was 13 (5-41) mL. Median ICV and TBV were 1377 (1283-1456) mL and 1108 (1020-1197) mL. Median ICVcore and TBVcore were 0.9 (0.4-2.8)% and 1.7 (0.5-3.6)%. Core volume (acOR per IQR 0.48 [95%CI 0.33-0.69]), ICVcore (acOR per IQR 0.50 [95%CI 0.35-0.69]), and TBVcore (acOR per IQR 0.41 95%CI 0.33-0.67]) showed a lower likelihood of achieving improved functional outcomes after 90 days. The AUC was 0.80 for the prediction of functional independence at 90 days for the CTP-estimated core volume, the ICVcore, and the TBVcore. CONCLUSION: Correcting the CTP-estimated core volume for the intracranial or total brain volume did not improve the association with functional outcomes in patients who underwent EVT.

Treatment Response Prediction in Major Depressive Disorder Using Multimodal MRI and Clinical Data: Secondary Analysis of a Randomized Clinical Trial.

OBJECTIVE: Response to antidepressant treatment in major depressive disorder varies substantially between individuals, which lengthens the process of finding effective treatment. The authors sought to determine whether a multimodal machine learning approach could predict early sertraline response in patients with major depressive disorder. They assessed the predictive contribution of MR neuroimaging and clinical assessments at baseline and after 1 week of treatment. METHODS: This was a preregistered secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a multisite double-blind, placebo-controlled randomized clinical trial that included 296 adult outpatients with unmedicated recurrent or chronic major depressive disorder. MR neuroimaging and clinical data were collected before and after 1 week of treatment. Performance in predicting response and remission, collected after 8 weeks, was quantified using balanced accuracy (bAcc) and area under the receiver operating characteristic curve (AUROC) scores. RESULTS: A total of 229 patients were included in the analyses (mean age, 38 years [SD=13]; 66% female). Internal cross-validation performance in predicting response to sertraline (bAcc=68% [SD=10], AUROC=0.73 [SD=0.03]) was significantly better than chance. External cross-validation on data from placebo nonresponders (bAcc=62%, AUROC=0.66) and placebo nonresponders who were switched to sertraline (bAcc=65%, AUROC=0.68) resulted in differences that suggest specificity for sertraline treatment compared with placebo treatment. Finally, multimodal models outperformed unimodal models. CONCLUSIONS: The study results confirm that early sertraline treatment response can be predicted; that the models are sertraline specific compared with placebo; that prediction benefits from integrating multimodal MRI data with clinical data; and that perfusion imaging contributes most to these predictions. Using this approach, a lean and effective protocol could individualize sertraline treatment planning to improve psychiatric care.

Artificial intelligence for assessment of vascular involvement and tumor resectability on CT in patients with pancreatic cancer.

OBJECTIVE: This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability. METHODS: We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients. After segmenting the relevant structures, our model quantifies vascular involvement by measuring the degree of the vessel wall that is in contact with the tumor using AI-segmented CTs. Based on these measurements, the model classifies the resectability stage using the Dutch Pancreatic Cancer Group criteria as either resectable, borderline resectable, or locally advanced (LA). RESULTS: We evaluated the performance of the model using a test set containing 60 CTs from 60 patients, consisting of 20 resectable, 20 borderline resectable, and 20 locally advanced cases, by comparing the automated analysis obtained from the model to expert visual vascular involvement assessments. The model concurred with the radiologists on 227/300 (76%) vessels for determining vascular involvement. The model's resectability classification agreed with the radiologists on 17/20 (85%) resectable, 16/20 (80%) for borderline resectable, and 15/20 (75%) for locally advanced cases. CONCLUSIONS: This study demonstrates that an AI model may allow automatic quantification of vascular involvement and classification of resectability for PDAC. RELEVANCE STATEMENT: This AI model enables automated vascular involvement quantification and resectability classification for pancreatic cancer, aiding radiologists in treatment decisions, and potentially improving patient outcomes. KEY POINTS: • High inter-observer variability exists in determining vascular involvement and resectability for PDAC. • Artificial intelligence accurately quantifies vascular involvement and classifies resectability for PDAC. • Artificial intelligence can aid radiologists by automating vascular involvement and resectability assessments.

Deep learning-based white matter lesion volume on CT is associated with outcome after acute ischemic stroke.

BACKGROUND: Intravenous thrombolysis (IVT) before endovascular treatment (EVT) for acute ischemic stroke might induce intracerebral hemorrhages which could negatively affect patient outcomes. Measuring white matter lesions size using deep learning (DL-WML) might help safely guide IVT administration. We aimed to develop, validate, and evaluate a DL-WML volume on CT compared to the Fazekas scale (WML-Faz) as a risk factor and IVT effect modifier in patients receiving EVT directly after IVT. METHODS: We developed a deep-learning model for WML segmentation on CT and validated with internal and external test sets. In a post hoc analysis of the MR CLEAN No-IV trial, we associated DL-WML volume and WML-Faz with symptomatic-intracerebral hemorrhage (sICH) and 90-day functional outcome according to the modified Rankin Scale (mRS). We used multiplicative interaction terms between WML measures and IVT administration to evaluate IVT treatment effect modification. Regression models were used to report unadjusted and adjusted common odds ratios (cOR/acOR). RESULTS: In total, 516 patients from the MR CLEAN No-IV trial (male/female, 291/225; age median, 71 [IQR, 62-79]) were analyzed. Both DL-WML volume and WML-Faz are associated with sICH (DL-WML volume acOR, 1.78 [95%CI, 1.17; 2.70]; WML-Faz acOR, 1.53 95%CI [1.02; 2.31]) and mRS (DL-WML volume acOR, 0.70 [95%CI, 0.55; 0.87], WML-Faz acOR, 0.73 [95%CI 0.60; 0.88]). Only in the unadjusted IVT effect modification analysis WML-Faz was associated with more sICH if IVT was given (p = 0.046). Neither WML measure was associated with worse mRS if IVT was given. CONCLUSION: DL-WML volume and WML-Faz had a similar relationship with functional outcome and sICH. Although more sICH might occur in patients with more severe WML-Faz receiving IVT, no worse functional outcome was observed. CLINICAL RELEVANCE STATEMENT: White matter lesion severity on baseline CT in acute ischemic stroke patients has a similar predictive value if measured with deep learning or the Fazekas scale. Safe administration of intravenous thrombolysis using white matter lesion severity should be further studied. KEY POINTS: White matter damage is a predisposing risk factor for intracranial hemorrhage in patients with acute ischemic stroke but remains difficult to measure on CT. White matter lesion volume on CT measured with deep learning had a similar association with symptomatic intracerebral hemorrhages and worse functional outcome as the Fazekas scale. A patient-level meta-analysis is required to study the benefit of white matter lesion severity-based selection for intravenous thrombolysis before endovascular treatment.

Radiomics for the prediction of a postoperative pancreatic fistula following a pancreatoduodenectomy: A systematic review and radiomic score quality assessment.

BACKGROUND: Postoperative pancreatic fistula (POPF) is a severe complication following a pancreatoduodenectomy. An accurate prediction of POPF could assist the surgeon in offering tailor-made treatment decisions. The use of radiomic features has been introduced to predict POPF. A systematic review was conducted to evaluate the performance of models predicting POPF using radiomic features and to systematically evaluate the methodological quality. METHODS: Studies with patients undergoing a pancreatoduodenectomy and radiomics analysis on computed tomography or magnetic resonance imaging were included. Methodological quality was assessed using the Radiomics Quality Score (RQS) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. RESULTS: Seven studies were included in this systematic review, comprising 1300 patients, of whom 364 patients (28 %) developed POPF. The area under the curve (AUC) of the included studies ranged from 0.76 to 0.95. Only one study externally validated the model, showing an AUC of 0.89 on this dataset. Overall adherence to the RQS (31 %) and TRIPOD guidelines (54 %) was poor. CONCLUSION: This systematic review showed that high predictive power was reported of studies using radiomic features to predict POPF. However, the quality of most studies was poor. Future studies need to standardize the methodology. REGISTRATION: not registered.

Standardizing the estimation of ischemic regions can harmonize CT perfusion stroke imaging.

OBJECTIVES: We aimed to evaluate the real-world variation in CT perfusion (CTP) imaging protocols among stroke centers and to explore the potential for standardizing vendor software to harmonize CTP images. METHODS: Stroke centers participating in a nationwide multicenter healthcare evaluation were requested to share their CTP scan and processing protocol. The impact of these protocols on CTP imaging was assessed by analyzing data from an anthropomorphic phantom with center-specific vendor software with default settings from one of three vendors (A-C): IntelliSpace Portal, syngoVIA, and Vitrea. Additionally, standardized infarct maps were obtained using a logistic model. RESULTS: Eighteen scan protocols were studied, all varying in acquisition settings. Of these protocols, seven, eight, and three were analyzed with center-specific vendor software A, B, and C respectively. The perfusion maps were visually dissimilar between the vendor software but were relatively unaffected by the acquisition settings. The median error [interquartile range] of the infarct core volumes (mL) estimated by the vendor software was - 2.5 [6.5] (A)/ - 18.2 [1.2] (B)/ - 8.0 [1.4] (C) when compared to the ground truth of the phantom (where a positive error indicates overestimation). Taken together, the median error [interquartile range] of the infarct core volumes (mL) was - 8.2 [14.6] before standardization and - 3.1 [2.5] after standardization. CONCLUSIONS: CTP imaging protocols varied substantially across different stroke centers, with the perfusion software being the primary source of differences in CTP images. Standardizing the estimation of ischemic regions harmonized these CTP images to a degree. CLINICAL RELEVANCE STATEMENT: The center that a stroke patient is admitted to can influence the patient's diagnosis extensively. Standardizing vendor software for CT perfusion imaging can improve the consistency and accuracy of results, enabling a more reliable diagnosis and treatment decision. KEY POINTS: • CT perfusion imaging is widely used for stroke evaluation, but variation in the acquisition and processing protocols between centers could cause varying patient diagnoses. • Variation in CT perfusion imaging mainly arises from differences in vendor software rather than acquisition settings, but these differences can be reconciled by standardizing the estimation of ischemic regions. • Standardizing the estimation of ischemic regions can improve CT perfusion imaging for stroke evaluation by facilitating reliable evaluations independent of the admission center.

Outcome of patients with multivessel occlusion stroke after endovascular treatment.

INTRODUCTION: Little is known about the implications of multivessel occlusions (MVO) in large vessel occlusion stroke patients who undergo endovascular treatment (EVT). PATIENTS AND METHODS: We report data from the MR CLEAN Registry: a prospective, observational study on all stroke patients who underwent EVT in the Netherlands (March 2014-November 2017). We included patients with an intracranial target occlusion in the anterior circulation. An MVO was defined as an MCA occlusion (M1/M2) or intracranial ICA/ICA-T occlusion, with a concurrent second occlusion in the ACA or PCA territory confirmed on baseline CTA. To compare outcomes, we performed a 10:1 propensity score matching analysis with a logistic regression model including potential confounders. Outcome measures included 90-day functional outcome (modified Rankin Scale, mRS) and mortality. RESULTS: Of 2946 included patients, 71 patients (2.4%) had an MVO (87% concurrent ACA occlusion, 10% PCA occlusion, 3% ⩾3 occlusions). These patients were matched to 71 non-MVO patients. Before matching, MVO patients had a higher baseline NIHSS (median 18 vs 16, p = 0.001) and worse collateral status (absent collaterals: 17% vs 6%, p < 0.001) compared to non-MVO patients. After matching, MVO patients had worse functional outcome at 90 days (median mRS 5 vs 3, cOR 0.39; 95%CI 0.25-0.62). Mortality was higher in MVO patients (46% vs 27%, OR 2.11, 95%CI 1.24-3.57). DISCUSSION AND CONCLUSION: MVOs on baseline imaging were uncommon in LVO stroke patients undergoing EVT, but were associated with poor functional outcome.

Costs and health effects of CT perfusion-based selection for endovascular thrombectomy within 6 hours of stroke onset: a model-based health economic evaluation.

BACKGROUND: Although CT perfusion (CTP) is often incorporated in acute stroke workflows, it remains largely unclear what the associated costs and health implications are in the long run of CTP-based patient selection for endovascular treatment (EVT) in patients presenting within 6 hours after symptom onset with a large vessel occlusion. METHODS: Patients with a large vessel occlusion were included from a Dutch nationwide cohort (n=703) if CTP imaging was performed before EVT within 6 hours after stroke onset. Simulated cost and health effects during 5 and 10 years follow-up were compared between CTP based patient selection for EVT and providing EVT to all patients. Outcome measures were the net monetary benefit at a willingness-to-pay of €80 000 per quality-adjusted life year, incremental cost-effectiveness ratio), difference in costs from a healthcare payer perspective (ΔCosts) and quality-adjusted life years (ΔQALY) per 1000 patients for 1000 model iterations as outcomes. RESULTS: Compared with treating all patients, CTP-based selection for EVT at the optimised ischaemic core volume (ICV≥110 mL) or core-penumbra mismatch ratio (MMR≤1.4) thresholds resulted in losses of health (median ΔQALYs for ICV≥110 mL: -3.3 (IQR: -5.9 to -1.1), for MMR≤1.4: 0.0 (IQR: -1.3 to 0.0)) with median ΔCosts for ICV≥110 mL of -€348 966 (IQR: -€712 406 to -€51 158) and for MMR≤1.4 of €266 513 (IQR: €229 403 to €380 110)) per 1000 patients. Sensitivity analyses did not yield any scenarios for CTP-based selection of patients for EVT that were cost-effective for improving health, including patients aged ≥80 years CONCLUSION: In EVT-eligible patients presenting within 6 hours after symptom onset, excluding patients based on CTP parameters was not cost-effective and could potentially harm patients.

Identifying Genetic Mutation Status in Patients with Colorectal Cancer Liver Metastases Using Radiomics-Based Machine-Learning Models.

For patients with colorectal cancer liver metastases (CRLM), the genetic mutation status is important in treatment selection and prognostication for survival outcomes. This study aims to investigate the relationship between radiomics imaging features and the genetic mutation status (KRAS mutation versus no mutation) in a large multicenter dataset of patients with CRLM and validate these findings in an external dataset. Patients with initially unresectable CRLM treated with systemic therapy of the randomized controlled CAIRO5 trial (NCT02162563) were included. All CRLM were semi-automatically segmented in pre-treatment CT scans and radiomics features were calculated from these segmentations. Additionally, data from the Netherlands Cancer Institute (NKI) were used for external validation. A total of 255 patients from the CAIRO5 trial were included. Random Forest, Gradient Boosting, Gradient Boosting + LightGBM, and Ensemble machine-learning classifiers showed AUC scores of 0.77 (95%CI 0.62-0.92), 0.77 (95%CI 0.64-0.90), 0.72 (95%CI 0.57-0.87), and 0.86 (95%CI 0.76-0.95) in the internal test set. Validation of the models on the external dataset with 129 patients resulted in AUC scores of 0.47-0.56. Machine-learning models incorporating CT imaging features could identify the genetic mutation status in patients with CRLM with a good accuracy in the internal test set. However, in the external validation set, the models performed poorly. External validation of machine-learning models is crucial for the assessment of clinical applicability and should be mandatory in all future studies in the field of radiomics.

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.

BACKGROUND: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metastases (CRLM). METHODS: In this prospective cohort study, pre- and post-systemic treatment computed tomography (CT) scans of 259 patients with initially unresectable CRLM of the CAIRO5 trial (NCT02162563) were included. In total, 595 CT scans comprising 8,959 CRLM were divided into training (73%), validation (6.5%), and test sets (21%). Deep learning models were trained with ground truth segmentations of the liver and CRLM. TTV was calculated based on the CRLM segmentations. An external validation cohort was included, comprising 72 preoperative CT scans of patients with 112 resectable CRLM. Image segmentation evaluation metrics and intraclass correlation coefficient (ICC) were calculated. RESULTS: In the test set (122 CT scans), the autosegmentation models showed a global Dice similarity coefficient (DSC) of 0.96 (liver) and 0.86 (CRLM). The corresponding median per-case DSC was 0.96 (interquartile range [IQR] 0.95-0.96) and 0.80 (IQR 0.67-0.87). For tumor segmentation, the intersection-over-union, precision, and recall were 0.75, 0.89, and 0.84, respectively. An excellent agreement was observed between the reference and automatically computed TTV for the test set (ICC 0.98) and external validation cohort (ICC 0.98). In the external validation, the global DSC was 0.82 and the median per-case DSC was 0.60 (IQR 0.29-0.76) for tumor segmentation. CONCLUSIONS: Deep learning autosegmentation models were able to segment the liver and CRLM automatically and accurately in patients with initially unresectable CRLM, enabling automatic TTV assessment in such patients. RELEVANCE STATEMENT: Automatic segmentation enables the assessment of total tumor volume in patients with colorectal liver metastases, with a high potential of decreasing radiologist's workload and increasing accuracy and consistency. KEY POINTS: • Tumor response evaluation is time-consuming, manually performed, and ignores total tumor volume. • Automatic models can accurately segment tumors in patients with colorectal liver metastases. • Total tumor volume can be accurately calculated based on automatic segmentations.

Subhairline Electroencephalography for the Detection of Large Vessel Occlusion Stroke.

BACKGROUND: Endovascular thrombectomy is standard treatment for patients with anterior circulation large vessel occlusion stroke (LVO-a). Prehospital identification of these patients would enable direct routing to an endovascular thrombectomy-capable hospital and consequently reduce time-to-endovascular thrombectomy. Electroencephalography (EEG) has previously proven to be promising for LVO-a stroke detection. Fast and reliable electrode application, however, can remain a challenge. A potential alternative is subhairline EEG. We evaluated the diagnostic accuracy of subhairline EEG for LVO-a stroke detection. METHODS AND RESULTS: We included adult patients with a suspected stroke or known LVO-a stroke and symptom onset time <24 hours. A single 3-minute EEG recording was performed at the emergency department, before endovascular thrombectomy, using 9 self-adhesive electrodes placed on the forehead and behind the ears. We evaluated the diagnostic accuracies of EEG features quantifying frequency band power and brain symmetry (pairwise derived Brain Symmetry Index) for LVO-a stroke detection using receiver operating characteristic analysis. EEG data were of sufficient quality for analysis in 51/52 (98%) included patients. Of these patients, 16 (31%) had an LVO-a stroke, 16 (31%) a non-LVO-a ischemic stroke, 5 (10%) a transient ischemic attack, and 14 (27%) a stroke mimic. Median symptom-onset-to-EEG-time was 266 (interquartile range 130-709) minutes. The highest diagnostic accuracy for LVO-a stroke detection was reached by the pairwise derived Brain Symmetry Index in the theta frequency band (area under the receiver operating characteristic curve 0.90; sensitivity 86%; specificity 83%). CONCLUSIONS: Subhairline EEG could detect LVO-a stroke with high diagnostic accuracy and had high data reliability. These data suggest that subhairline EEG is potentially suitable as a prehospital stroke triage instrument.

Spatio-temporal physics-informed learning: A novel approach to CT perfusion analysis in acute ischemic stroke.

CT perfusion imaging is important in the imaging workup of acute ischemic stroke for evaluating affected cerebral tissue. CT perfusion analysis software produces cerebral perfusion maps from commonly noisy spatio-temporal CT perfusion data. High levels of noise can influence the results of CT perfusion analysis, necessitating software tuning. This work proposes a novel approach for CT perfusion analysis that uses physics-informed learning, an optimization framework that is robust to noise. In particular, we propose SPPINN: Spatio-temporal Perfusion Physics-Informed Neural Network and research spatio-temporal physics-informed learning. SPPINN learns implicit neural representations of contrast attenuation in CT perfusion scans using the spatio-temporal coordinates of the data and employs these representations to estimate a continuous representation of the cerebral perfusion parameters. We validate the approach on simulated data to quantify perfusion parameter estimation performance. Furthermore, we apply the method to in-house patient data and the public Ischemic Stroke Lesion Segmentation 2018 benchmark data to assess the correspondence between the perfusion maps and reference standard infarct core segmentations. Our method achieves accurate perfusion parameter estimates even with high noise levels and differentiates healthy tissue from infarcted tissue. Moreover, SPPINN perfusion maps accurately correspond with reference standard infarct core segmentations. Hence, we show that using spatio-temporal physics-informed learning for cerebral perfusion estimation is accurate, even in noisy CT perfusion data. The code for this work is available at https://github.com/lucasdevries/SPPINN.

Prehospital Detection of Large Vessel Occlusion Stroke With EEG.

BACKGROUND AND OBJECTIVES: Endovascular thrombectomy (EVT) is standard treatment for anterior large vessel occlusion stroke (LVO-a stroke). Prehospital diagnosis of LVO-a stroke would reduce time to EVT by allowing direct transportation to an EVT-capable hospital. We aim to evaluate the diagnostic accuracy of dry electrode EEG for the detection of LVO-a stroke in the prehospital setting. METHODS: ELECTRA-STROKE was an investigator-initiated, prospective, multicenter, diagnostic study, performed in the prehospital setting. Adult patients were eligible if they had suspected stroke (as assessed by the attending ambulance nurse) and symptom onset <24 hours. A single dry electrode EEG recording (8 electrodes) was performed by ambulance personnel. Primary endpoint was the diagnostic accuracy of the theta/alpha frequency ratio for LVO-a stroke (intracranial ICA, A1, M1, or proximal M2 occlusion) detection among patients with EEG data of sufficient quality, expressed as the area under the receiver operating characteristic curve (AUC). Secondary endpoints were diagnostic accuracies of other EEG features quantifying frequency band power and the pairwise derived Brain Symmetry Index. Neuroimaging was assessed by a neuroradiologist blinded to EEG results. RESULTS: Between August 2020 and September 2022, 311 patients were included. The median EEG duration time was 151 (interquartile range [IQR] 151-152) seconds. For 212/311 (68%) patients, EEG data were of sufficient quality for analysis. The median age was 74 (IQR 66-81) years, 90/212 (42%) were women, and the median baseline NIH Stroke Scale was 1 (IQR 0-4). Six (3%) patients had an LVO-a stroke, 109/212 (51%) had a non-LVO-a ischemic stroke, 32/212 (15%) had a transient ischemic attack, 8/212 (4%) had a hemorrhagic stroke, and 57/212 (27%) had a stroke mimic. AUC of the theta/alpha ratio was 0.80 (95% CI 0.58-1.00). Of the secondary endpoints, the pairwise derived Brain Symmetry Index in the delta frequency band had the highest diagnostic accuracy (AUC 0.91 [95% CI 0.73-1.00], sensitivity 80% [95% CI 38%-96%], specificity 93% [95% CI 88%-96%], positive likelihood ratio 11.0 [95% CI 5.5-21.7]). DISCUSSION: The data from this study suggest that dry electrode EEG has the potential to detect LVO-a stroke among patients with suspected stroke in the prehospital setting. Toward future implementation of EEG in prehospital stroke care, EEG data quality needs to be improved. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov identifier: NCT03699397. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that prehospital dry electrode scalp EEG accurately detects LVO-a stroke among patients with suspected acute stroke.

Radiomics preoperative-Fistula Risk Score (RAD-FRS) for pancreatoduodenectomy: development and external validation.

BACKGROUND: Accurately predicting the risk of clinically relevant postoperative pancreatic fistula after pancreatoduodenectomy before surgery may assist surgeons in making more informed treatment decisions and improved patient counselling. The aim was to evaluate the predictive accuracy of a radiomics-based preoperative-Fistula Risk Score (RAD-FRS) for clinically relevant postoperative pancreatic fistula. METHODS: Radiomic features were derived from preoperative CT scans from adult patients after pancreatoduodenectomy at a single centre in the Netherlands (Amsterdam, 2013-2018) to develop the radiomics-based preoperative-Fistula Risk Score. Extracted radiomic features were analysed with four machine learning classifiers. The model was externally validated in a single centre in Italy (Verona, 2020-2021). The radiomics-based preoperative-Fistula Risk Score was compared with the Fistula Risk Score and the updated alternative Fistula Risk Score. RESULTS: Overall, 359 patients underwent a pancreatoduodenectomy, of whom 89 (25 per cent) developed a clinically relevant postoperative pancreatic fistula. The radiomics-based preoperative-Fistula Risk Score model was developed using CT scans of 118 patients, of which three radiomic features were included in the random forest model, and externally validated in 57 patients. The model performed well with an area under the curve of 0.90 (95 per cent c.i. 0.71 to 0.99) and 0.81 (95 per cent c.i. 0.69 to 0.92) in the Amsterdam test set and Verona data set respectively. The radiomics-based preoperative-Fistula Risk Score performed similarly to the Fistula Risk Score (area under the curve 0.79) and updated alternative Fistula Risk Score (area under the curve 0.79). CONCLUSION: The radiomics-based preoperative-Fistula Risk Score, which uses only preoperative CT features, is a new and promising radiomics-based score that has the potential to be integrated with hospital CT report systems and improve patient counselling before surgery. The model with underlying code is readily available via www.pancreascalculator.com and www.github.com/PHAIR-Consortium/POPF-predictor.

Temporal validation of 30-day mortality prediction models for transcatheter aortic valve implantation using statistical process control - An observational study in a national population.

Background: Various mortality prediction models for Transcatheter Aortic Valve Implantation (TAVI) have been developed in the past years. The effect of time on the performance of such models, however, is unclear given the improvements in the procedure and changes in patient selection, potentially jeopardizing the usefulness of the prediction models in clinical practice. We aim to explore how time affects the performance and stability of different types of prediction models of 30-day mortality after TAVI. Methods: We developed both parametric (Logistic Regression) and non-parametric (XGBoost) models to predict 30-day mortality after TAVI using data from the Netherlands Heart Registration. The models were trained with data from 2013 to the beginning of 2016 and pre-control charts from Statistical Process Control were used to analyse how time affects the models' performance on independent data from the mid of 2016 to the end of 2019. The area under the Receiver Operating Characteristics curve (AUC) was used to evaluate the models in terms of discrimination and the Brier Score (BS), which is related to calibration, in terms of accuracy of the predicted probabilities. To understand the extent to which refitting the models contribute to the models' stability, we also allowed the models to be updated over time. Results: We included data from 11,291 consecutive TAVI patients from hospitals in the Netherlands. The parametric model without re-training had a median AUC of 0.64 (IQR 0.54-0.73) and BS of 0.028 (IQR 0.021-0.035). For the non-parametric model, the median AUC was 0.63 (IQR 0.48-0.68) and BS was 0.027 (IQR 0.021-0.036). Over time, the developed parametric model was stable in terms of AUC and unstable in terms of BS. The non-parametric model was considered unstable in both AUC and BS. Repeated model refitting resulted in stable models in terms of AUC and decreased the variability of BS, although BS was still unstable. The refitted parametric model had a median AUC of 0.66 (IQR 0.57-0.73) and BS of 0.027 (IQR 0.020-0.035) while the non-parametric model had a median AUC of 0.66 (IQR 0.57-0.74) and BS of 0.027 (IQR 0.023-0.035). Conclusions: The temporal validation of the TAVI 30-day mortality prediction models showed that the models refitted over time are more stable and accurate when compared to the frozen models. This highlights the importance of repeatedly refitted models over time to improve or at least maintain their performance stability. The non-parametric approach did not show improvement over the parametric approach.

Impact of vessel tortuosity and radiological thrombus characteristics on the choice of first-line thrombectomy strategy: Results from the ESCAPE-NA1 trial.

INTRODUCTION: Despite improvements in device technology, only one-third of stroke patients undergoing endovascular thrombectomy (EVT) achieve first-pass effect (FPE). We investigated the effect of arterial tortuosity and thrombus characteristics on the relationship between first-line EVT strategy and angiographic outcomes. PATIENTS AND METHODS: Patients with thin-slice baseline CT-angiography from the ESCAPE-NA1 trial (Efficacy and safety of nerinetide for the treatment of acute ischemic stroke) were included. Tortuosity was estimated using the tortuosity index extracted from catheter pathway, and radiological thrombus characteristics were length, non-contrast density, perviousness and hyperdense artery sign. We assessed the association of first-line EVT strategy (stent-retriever [SR] versus contact aspiration [CA] versus combined SR+CA) with FPE (eTICI score 2c/3 after one pass), final eTICI 2b/3, number of passes and procedure duration using multivariable regression. Interaction of tortuosity and thrombus characteristics with first-line technique were assessed using interaction terms. RESULTS: Among 520 included patients, SR as a first-line modality was used in 165 (31.7%) patients, CA in 132 (25.4%), and combined SR+CA in 223 (42.9%). FPE was observed in 166 patients (31.9%). First-line strategy was not associated with FPE. Tortuosity had a significant effect on FPE only in the CA group (aOR = 0.90 [95% CI 0.83-0.98]) compared with stent-retrievers and combined first-line approach (p interaction = 0.03). There was an interaction between thrombus length and first-line strategy for number of passes (p interaction = 0.04). Longer thrombi were associated with higher number of passes only in the CA group (acOR 1.03 [95% CI 1.00-1.06]). CONCLUSION: Our study suggests that vessel tortuosity and longer thrombi may negatively affect the performance of first-line contact aspiration catheters in acute stroke patients undergoing EVT.