Risultati per: L'imaging nella urolitiasi

Stroke, Ahead of Print. Background and Purpose:The optimal imaging paradigm for endovascular thrombectomy (EVT) patient selection in early time window (0–6 hours) treated acute ischemic stroke patients remains uncertain. We aimed to compare post-EVT outcomes between patients who underwent prerandomization basic (noncontrast computed tomography [CT], CT angiography only) versus additional advanced imaging (computed tomography perfusion [CTP] imaging) and to determine the association of performance of prerandomization CTP imaging with clinical outcomes.Methods:The HERMES collaboration (Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials) pooled patient-level data from randomized controlled trials comparing EVT with usual care for acute ischemic stroke due to anterior circulation large vessel occlusion. Good functional outcome, defined as modified Rankin Scale score 0 to 2 at 90 days, was compared between randomized patients with and without CTP baseline imaging. Univariable and multivariable binary logistic regression analysis was performed to determine the association of baseline CTP imaging and good functional outcome.Results:We analyzed 1348 patients 610 (45.3%) of whom underwent CTP prerandomization. The benefit of EVT compared with best medical management was maintained irrespective of the baseline imaging paradigm (90-day modified Rankin Scale score 0–2 in EVT versus control patients: with CTP: 46.0% (137/298) versus 28.9% (88/305), without CTP: 44.1% (162/367) versus 27.3% (100/366). Performance of CTP baseline imaging compared with baseline noncontrast CT and CT angiography only yielded similar rates of good outcome (odds ratio, 1.05 [95% CI, 0.82–1.33], adjusted odds ratio, 1.04, [95% CI, 0.80–1.35]).Conclusions:Rates of good functional outcome were similar among patients in whom CTP was or was not performed, and EVT treatment effect in the 0- to 6-hour time window was similar in patients with and without baseline CTP imaging.

Stroke, Volume 53, Issue 2, Page 569-577, February 1, 2022. Background and Purpose:Computed tomography perfusion imaging allows estimation of tissue status in patients with acute ischemic stroke. We aimed to improve prediction of the final infarct and individual infarct growth rates using a deep learning approach.Methods:We trained a deep neural network to predict the final infarct volume in patients with acute stroke presenting with large vessel occlusions based on the native computed tomography perfusion images, time to reperfusion and reperfusion status in a derivation cohort (MR CLEAN trial [Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands]). The model was internally validated in a 5-fold cross-validation and externally in an independent dataset (CRISP study [CT Perfusion to Predict Response to Recanalization in Ischemic Stroke Project]). We calculated the mean absolute difference between the predictions of the deep learning model and the final infarct volume versus the mean absolute difference between computed tomography perfusion imaging processing by RAPID software (iSchemaView, Menlo Park, CA) and the final infarct volume. Next, we determined infarct growth rates for every patient.Results:We included 127 patients from the MR CLEAN (derivation) and 101 patients of the CRISP study (validation). The deep learning model improved final infarct volume prediction compared with the RAPID software in both the derivation, mean absolute difference 34.5 versus 52.4 mL, and validation cohort, 41.2 versus 52.4 mL (P

The Publisher regrets that this article is an accidental duplication of an article that has already been published, DOI of original article: http://dx.doi.org/10.1053/j.gastro.2011.01.058.The duplicate article has therefore been withdrawn.