Browsing by Author "Menon, Bijoy K."

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    A Psychological Perspective on Image Interpretation in Acute Ischemic Stroke: Factors Affecting Non-Contrast CT ASPECTS Reliability
    (2018-06-27) Wilson, Alexis Terrin Connett; Menon, Bijoy K.; Demchuk, Andrew M.; Hill, Michael D.; Saposnik, Gustavo
    The Alberta Stroke Program Early CT Score (ASPECTS) is a semiquantitative scale to assess the extent of early ischemic changes on non-contrast CT in acute ischemic stroke patients. This is crucial for prognostication and treatment selection. Recent studies have revealed significant heterogeneity in reported measures of inter-rater reliability in ASPECTS, and this thesis aims to investigate the reasons underlying this phenomenon from the perspective of clinicians’ cognitive processes. First, this work explores relevant topics in the psychology of image interpretation and, on this psychological basis, proposes potential causes of inconsistent ASPECTS reliability. Possible strategies to improve clinicians’ inter- and intra-rater reliability are also discussed. The effect of image reading context variables and rater expertise on ASPECTS inter-rater reliability was then investigated. Raters of different experience levels scored ASPECTS on baseline non-contrast CT scans under three prior-information conditions (NCCT only, NCCT with access to clinical information, NCCT with access to clinical information and multiphase CT angiography) and three reading-context conditions (high/low ambient light, time pressure). The results indicate that these variables have the capacity to affect ASPECTS reliability. This work highlights the importance of acknowledging that medical image interpretation can be influenced by seemingly irrelevant external and internal factors like reading environment characteristics or physician-level variables. Giving more consideration to these variables in clinical and educational settings could improve the utility of tools like ASPECTS.
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    Machine learning models for functional impairment risk prediction in ischemic stroke patients
    (2020-09-03) Alaka, Shakiru Ayomide; Sajobi, Tolulope T.; Menon, Bijoy K.; Hill, Michael D.; Williamson, Tyler S.
    Background: Stroke-related functional impairment risk scores are commonly used to estimate the patient-specific risk of functional impairment in acute care settings. However, these models have been primarily developed based on regression models, which might not provide optimal predictive accuracy, especially when validated in an external cohort. Purpose: To evaluate the predictive accuracy of machine-learning (ML) models for predicting functional impairment risk in acute ischemic stroke patients. Second, to compare the predictive accuracy of machine-learning models and regression-based models using computer simulations. Methods: Using data from the Precise and Rapid Assessment of Collaterals with Multi-phase CT Angiography (PROVE-IT). The Modified Rankin Scale (mRS) score was used to assess the 90-day functional impairment status. The accuracy of machine-learning models such as random forest (RF), classification and regression tree (CART), support vector machine (SVM), C5.0 decision tree (DT), adaptive boost machine (ABM), and least absolute shrinkage and selection operator (LASSO) logistic regression, and logistic regression (LR) was used to predict the risk of patient-specific risk of 90-day functional impairment. Area under the receiver operating characteristic curve (AUC) sensitivity, specificity, Mathews correlation coefficient (MCC) and Brier score was used to assess the predictive accuracy of these models via internal cross-validation and external validation in the Identifying New Approaches to Optimize Thrombus Characterization for Predicting Early Recanalization and Reperfusion with IVtPA Using Serial CT Angiography (INTERSSeCT) cohort study. Monte Carlo methods were used to develop recommendations for selecting machine-learning models under a variety of data characteristics. Results: Both logistic regression and machine-learning models had comparable predictive accuracy when validated internally (AUC range = [0.65 – 0.72]; MCC range = [0.29 - 0.42]) and externally (AUC range = [0.66 – 0.71]; MCC range = [0.34 – 0.42]). However, regression-based had a fairly better calibration than the ML models. Our simulation study showed that ML and regression-based models are not equally robust to a variety of data analytic characteristics. LR models exhibited higher AUC in studies with a small/moderate set of predictors, while RF had about 15% higher discrimination studies with high dimensional set of predictors. ML models may be less accurate for predicting outcomes in studies with a few sets of predictors or when there is a large class imbalance in the data sets. Conclusions ML and regression-based algorithms are not equally sensitive to data analytic conditions, even though our data analysis revealed no significant differences between the former and the latter. ML might offer some discriminative advantages over the latter depending on the size and type of study predictors. We recommend that the choice between these classes of models should be guided by data characteristics, study design, and purpose for which the models are being developed.
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    Post-ictal hypoperfusion detected by CT Perfusion
    (2018-05-07) Li, Emmy; Federico, Paolo; Teskey, Gordon Campbell; Lee, Ting Yim; Menon, Bijoy K.
    Background: Seizures are often followed by a period of transient neurological dysfunction whereby sensory, cognitive, or motor abilities are impaired. Alterations in cerebral blood flow (CBF) during the postictal period has been proposed as a possible mechanism for this phenomenon. Recent animal studies have shown reduced local CBF at the seizure onset zone (SOZ) lasting up to one hour following seizures (Farrell, et al., 2016). Using arterial spin labeling magnetic resonance imaging (ASL MRI), we have observed postictal hypoperfusion at the SOZ in 75% of patients lasting up to one hour (Gaxiola-Valdez, 2017). The clinical implementation of ASL as a novel tool to identify the SOZ is hampered by the limited availability of MRI on short notice. Computed tomography perfusion (CTP) also measures CBF changes and may circumvent the logistical limitations of MRI. Methods: Fifteen patients with drug resistant focal epilepsy admitted for pre-surgical evaluation were prospectively recruited and underwent CTP scanning within 80 min of a habitual seizure. Patients underwent a second scan in the interictal period after they were seizure-free for at least 24 hours. The acquired scans were visually assessed for perfusion differences and quantitatively assessed to identify areas of significant postictal hypoperfusion. Results: Postictal reductions of >15 CBF units (ml/100g-1/min-1) were seen in 12/15 patients. In 10 of these patients, the location of the hypoperfusion was partially or fully concordant with the presumed SOZ, and all patients localized additional areas of seizure propagation concordant with their electroencephalography (EEG). Conclusions: Postictal hypoperfusion can be measured by CTP. Thus, CTP has the potential to be a cost-effective and readily available tool in localizing the SOZ by measuring postictal CBF changes.
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    Predicting the Risk of Intracerebral Haemorrhage in Patients with Acute Ischemic Stroke Receiving IV-alteplase with or Without Endovascular Thrombectomy
    (2016) Batchelor, Connor; Menon, Bijoy K.; Demchuk, Andrew M.; Goyal, Mayank; Lee, Ting-Yim; Sajobi, Tolulope
    Intracerebral Haemorrhage (ICH) is a devastating complication of acute ischemic stroke (AIS) treatment with no known effective management protocols. The need to identify patients at risk of developing this condition is becoming increasingly recognized among the stroke community. Computed tomography perfusion (CTP) is a powerful diagnostic imaging tool that measures blood flow in the brain. This tool can also be used to provide information regarding the integrity of the blood-brain barrier (BBB). Severe brain ischemia and consequent disruption of the BBB are probable mechanisms for why ICH occurs after AIS treatment. The goal of my research is to investigate the potential role of CTP primarily and other imaging and clinical parameters in predicting ICH secondary to AIS treatment in patients.
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    Testing ASPECTS Reliability Using Color Coded Algorithm Enhanced Gray- White Matter Non Contrast CT
    (2018-07-09) Hafeez, Moiz; Menon, Bijoy K.; Qiu, Wu; Federico, Paolo; Demchuk, Andrew M.; Krupinski, Elizabeth A.; Sajobi, Tolulope T.
    The Alberta Stroke Program Early CT Score (ASPECTS) is widely used to assess and diagnose Acute Ischemic Stroke Patients (AIS). Inter-rater reliability for ASPECTS however, is very poor even amongst physicians with extensive expertise. Much of this limitation has to do with the lack of agreement amongst physicians in identifying Early Ischemic Changes (EIC) on Non- Contrast Computed Tomography (NCCT) scans. This lack of agreement is due to the extremely subtle findings that the human eye is exposed to on gray scale NCCT scans during the acute period of ischemia. We therefore sought to use post processing algorithms to develop Color- Coded Algorithm Enhanced Gray- White Matter (AEGWM) NCCT scans. Increased differentiation between Gray- White matter on AEGWM NCCT scans was developed to act as a powerful imaging tool allowing for better delineation of EIC for AIS patients. In this thesis I investigated the utility of AEGWM NCCT scans for the purposes of detecting EIC in AIS patients. Overall, we found that AEGWM scans performed better as opposed to gray scale NCCT scans when using DWI as ground truth. In addition, inter rater agreement increased consistently across raters of all levels of expertise while using AEGWM scans. Although with some limitations, the use of AEGWM scans may be a promising research direction to pursue for future work.