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BACKGROUND: Application of machine learning (ML) algorithms has shown promising results in estimating ischemic core volumes using non-contrast CT (NCCT). OBJECTIVE: To assess the performance of the e-Stroke Suite software (Brainomix) in assessing ischemic core volumes on NCCT compared with CT perfusion (CTP) in patients with acute ischemic stroke. METHODS: In this retrospective multicenter study, patients with anterior circulation large vessel occlusions who underwent pretreatment NCCT and CTP, successful reperfusion (modified Thrombolysis in Cerbral Infarction ≥2b), and post-treatment MRI, were included from three stroke centers. Automated calculation of ischemic core volumes was obtained on NCCT scans using ML algorithm deployed by e-Stroke Suite and from CTP using Olea software (Olea Medical). Comparative analysis was performed between estimated core volumes on NCCT and CTP and against MRI calculated final infarct volume (FIV). RESULTS: A total of 111 patients were included. Estimated ischemic core volumes (mean±SD, mL) were 20.4±19.0 on NCCT and 19.9±18.6 on CTP, not significantly different (P=0.82). There was moderate (r=0.40) and significant (P<0.001) correlation between estimated core on NCCT and CTP. The mean difference between FIV and estimated core volume on NCCT and CTP was 29.9±34.6 mL and 29.6±35.0 mL, respectively (P=0.94). Correlations between FIV and estimated core volume were similar for NCCT (r=0.30, P=0.001) and CTP (r=0.36, P<0.001). CONCLUSIONS: Results show that ML-based estimated ischemic core volumes on NCCT are comparable to those obtained from concurrent CTP in magnitude and in degree of correlation with MR-assessed FIV.
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BACKGROUND: Accurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine noncontrast computed tomography (NCCT). OBJECTIVE: We used an ML-trained algorithm to quantify ischemic core volume on NCCT in a comparative analysis to pretreatment magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) in patients with AIS. METHODS: Patients with AIS who had both pretreatment NCCT and MRI were enrolled. An automatic segmentation ML approach was applied using Brainomix software (Oxford, UK) to segment the ischemic voxels and calculate ischemic core volume on NCCT. Ischemic core volume was also calculated on baseline MRI DWI. Comparative analysis was performed using Bland-Altman plots and Pearson correlation. RESULTS: A total of 72 patients were included. The time-to-stroke onset time was 134.2/89.5 minutes (mean/median). The time difference between NCCT and MRI was 64.8/44.5 minutes (mean/median). In patients who presented within 1 hour from stroke onset, the ischemic core volumes were significantly (p = 0.005) underestimated by ML-NCCT. In patients presented beyond 1 hour, the ML-NCCT estimated ischemic core volumes approximated those obtained by MRI-DWI and with significant correlation (r = 0.56, p < 0.001). CONCLUSION: The ischemic core volumes calculated by the described ML approach on NCCT approximate those obtained by MRI in patients with AIS who present beyond 1 hour from stroke onset.
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Collateral status has prognostic and treatment implications in acute ischemic stroke (AIS) patients. Unlike CTA, grading collaterals on MRA is not well studied. We aimed to evaluate the accuracy of assessing collaterals on pretreatment MRA in AIS patients against DSA. AIS patients with anterior circulation proximal arterial occlusion with baseline MRA and subsequent endovascular treatment were included. MRA collaterals were evaluated by two neuroradiologists independently using the Tan and Maas scoring systems. DSA collaterals were evaluated by using the American Society of Interventional and Therapeutic Neuroradiology grading system and were used as the reference for comparative analysis against MRA. A total of 104 patients met the inclusion criteria (59 female, age (mean ± SD): 70.8 ± 18.1). The inter-rater agreement (k) for collateral scoring was 0.49, 95% CI 0.37-0.61 for the Tan score and 0.44, 95% CI 0.26-0.62 for the Maas score. Total number (%) of sufficient vs. insufficient collaterals based on DSA was 49 (47%) and 55 (53%) respectively. Using the Tan score, 45% of patients with sufficient collaterals and 64% with insufficient collaterals were correctly identified in comparison to DSA, resulting in a poor agreement (0.09, 95% CI 0.1-0.28). Using the Maas score, only 4% of patients with sufficient collaterals and 93% with insufficient collaterals were correctly identified against DSA, resulting in poor agreement (0.03, 95% CI 0.06-0.13). Pretreatment MRA in AIS patients has limited concordance with DSA when grading collaterals using the Tan and Maas scoring systems.
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Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The 'purification power' of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80-250 Hz) and fast ripple (250-500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model's decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge.
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Estrogen and estrogen receptors (ERs) are critical regulators of breast epithelial cell proliferation, differentiation, and apoptosis. Compromised signaling vis-à-vis the estrogen receptor is believed to be a major contributing factor in the malignancy of breast cells. Targeting the ER signaling pathway has been a focal point in the development of breast cancer therapy. Although approximately 75 % of breast cancer patients are classified as luminal type (ER(+)), which predicts for response to endocrine-based therapy; however, innate or acquired resistance to endocrine-based drugs remains a serious challenge. The complexity of regulation for estrogen signaling coupled with the crosstalk of other oncogenic signaling pathways is a reason for endocrine therapy resistance. Alternative strategies that target novel molecular mechanisms are necessary to overcome this current and urgent gap in therapy. A thorough analysis of estrogen-signaling regulation is critical. In this review article, we will summarize current insights into the regulation of estrogen signaling as related to breast carcinogenesis and breast cancer therapy.
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The purpose of the present study was to present a multi-delay multi-parametric pseudo-continuous arterial spin labeling (pCASL) protocol with background suppressed 3D GRASE (gradient and spin echo) readout for perfusion imaging in acute ischemic stroke. PCASL data at 4 post-labeling delay times (PLD = 1.5, 2, 2.5, 3 s) were acquired within 4.5 min in 24 patients (mean age 79.7 ± 11.4 years; 11 men) with acute middle cerebral artery (MCA) stroke who also underwent dynamic susceptibility contrast (DSC) enhanced perfusion imaging. Arterial transit times (ATT) were estimated through the calculation of weighted delays across the 4 PLDs, which were included in the calculation of cerebral blood flow (CBF) and arterial cerebral blood volume (CBV). Mean perfusion parameters derived using pCASL and DSC were measured within MCA territories and infarct regions identified on diffusion weighted MRI. The results showed highly significant correlations between pCASL and DSC CBF measurements (r > = 0.70, p < = 0.0001) and moderately significant correlations between pCASL and DSC CBV measurements (r > = 0.45, p < = 0.027) in both MCA territories and infarct regions. ASL ATT showed correlations with DSC time to the maximum of tissue residual function (Tmax)(r = 0.66, p = 0.0005) and mean transit time (MTT)(r = 0.59, p = 0.0023) in leptomeningeal MCA territories. The present study demonstrated the feasibility for noninvasive multi-parametric perfusion imaging using ASL for acute stroke imaging.
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BACKGROUND AND PURPOSE: The purpose of this study was to evaluate the potential clinical value of arterial spin-labeled (ASL) perfusion MRI in acute ischemic stroke (AIS) through comparison with dynamic susceptibility contrast (DSC) enhanced perfusion MRI. METHODS: Pseudocontinuous ASL with 3-dimensional background-suppressed gradient and spin echo readout was applied with DSC perfusion MRI on 26 patients with AIS. ASL cerebral blood flow and multiparametric DSC perfusion maps were rated for image quality and lesion severity/conspicuity. Mean ASL cerebral blood flow and DSC perfusion values were obtained in main vascular territories. Kendall coefficient of concordance was calculated to evaluate the reliability of ratings. Spearman correlation coefficients were calculated to compare ratings and quantitative perfusion values between ASL and DSC perfusion maps. RESULTS: ASL cerebral blood flow and DSC perfusion maps provided largely consistent results in delineating hypoperfused brain regions in AIS. Hyperemic lesions, which also appeared frequently in the AIS cases studied, were more conspicuous on ASL cerebral blood flow than on DSC cerebral blood flow, mean transit time and time to the maximum of the tissue residual function maps. CONCLUSIONS: As a rapid, noninvasive, and quantitative technique, ASL has clinical use in detecting blood flow abnormalities in patients with AIS.
