Assessment of computed tomography perfusion software in predicting spatial location and volume of infarct in acute ischemic stroke patients: a comparison of Sphere, Vitrea, and RAPID.

BACKGROUND: CT perfusion (CTP) infarct and penumbra estimations determine the eligibility of patients with acute ischemic stroke (AIS) for endovascular intervention. This study aimed to determine volumetric and spatial agreement of predicted RAPID, Vitrea, and Sphere CTP infarct with follow-up fluid attenuation inversion recovery (FLAIR) MRI infarct. METHODS: 108 consecutive patients with AIS and large vessel occlusion were included in the study between April 2019 and January 2020 . Patients were divided into two groups: endovascular intervention (n=58) and conservative treatment (n=50). Intervention patients were treated with mechanical thrombectomy and achieved successful reperfusion (Thrombolysis in Cerebral Infarction 2b/2 c/3) while patients in the conservative treatment group did not receive mechanical thrombectomy or intravenous thrombolysis. Intervention and conservative treatment patients were included to assess infarct and penumbra estimations, respectively. It was assumed that in all patients treated conservatively, penumbra converted to infarct. CTP infarct and penumbra volumes were segmented from RAPID, Vitrea, and Sphere to assess volumetric and spatial agreement with follow-up FLAIR MRI. RESULTS: Mean infarct differences (95% CIs) between each CTP software and FLAIR MRI for each cohort were: intervention cohort: RAPID=9.0±7.7 mL, Sphere=-0.2±8.7 mL, Vitrea=-7.9±8.9 mL; conservative treatment cohort: RAPID=-31.9±21.6 mL, Sphere=-26.8±17.4 mL, Vitrea=-15.3±13.7 mL. Overlap and Dice coefficients for predicted infarct were (overlap, Dice): intervention cohort: RAPID=(0.57, 0.44), Sphere=(0.68, 0.60), Vitrea=(0.70, 0.60); conservative treatment cohort: RAPID=(0.71, 0.56), Sphere=(0.73, 0.60), Vitrea=(0.72, 0.64). CONCLUSIONS: Sphere proved the most accurate in patients who had intervention infarct assessment as Vitrea and RAPID overestimated and underestimated infarct, respectively. Vitrea proved the most accurate in penumbra assessment for patients treated conservatively although all software overestimated penumbra.

Initial evaluation of a convolutional neural network used for noninvasive assessment of coronary artery disease severity from coronary computed tomography angiography data.

PURPOSE: Coronary computed tomography angiography (CTA) has one of the highest diagnostic sensitivities for detection of the significance of coronary artery disease (CAD); however, sensitivity is moderate and may result in increased catheterization rates. We performed an efficacy study to determine whether a trained machine learning algorithm that uses coronary CTA data may improve CAD diagnosis accuracy. METHODS: Sixty-four-patient image datasets based on coronary CTA were retrospectively collected to generate eight views considering 45° increments around the coronary artery centerline. The dataset was randomly split into training and testing cohorts. Invasive FFR measurements were used as ground truth labels. A convolutional neural network (CNN) was trained and the model's capacity to predict severity of CAD was assessed on the testing cohort. Classification accuracy and area under the receiver operating characteristic curve (AUROC) analysis were performed. Similar CAD severity classification accuracy and AUROC analyses were performed using only percent diameter stenosis (%DS) and CT-derived FFR performed by 13 operators with various levels of expertise. RESULTS: Classification accuracy over the test cohort was 80.9% using the trained network and 72.4% using the user-operated CT-derived FFR software. AUROC over the test cohort was 0.862 using the trained network, 0.807 using %DS, and 0.758 using the human-operated CT-derived FFR software. CONCLUSIONS: A trained neural network compared noninferiorly in-terms of classification accuracy and AUROC with human operators of a CT-derived FFR software, and in-terms of AUROC with clinical decision-making using %DS.