Diffusion-Weighted Imaging-Alone Endovascular Thrombectomy Triage in Acute Stroke: Simulating Diffusion-Perfusion Mismatch Using Machine Learning.
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SUBMITTER: Kim YC
PROVIDER: S-EPMC8829488 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
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Diffusion-Weighted Imaging-Alone Endovascular Thrombectomy Triage in Acute Stroke: Simulating Diffusion-Perfusion Mismatch Using Machine Learning.
Kim Yoon-Chul YC Seo Woo-Keun WK Baek In-Young IY Lee Ji-Eun JE Song Ha-Na HN Chung Jong-Won JW Kim Chi Kyung CK Oh Kyungmi K Suh Sang-Il SI Bang Oh Young OY Kim Gyeong-Moon GM Saver Jeffrey L JL Liebeskind David S DS
Journal of stroke 20220131 1
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Project description:Endovascular thrombectomy (EVT) is the standard of care for large vessel occlusion stroke. Use of Computed Tomographic Perfusion (CTP) to select EVT candidates is variable. The frequency of treatment and outcome in patients with unfavourable CTP patterns is unknown. A retrospective analysis of CTP utilisation prior to EVT was conducted. All CTP data were analysed centrally and a Target Mismatch was defined as an infarct core ≤70 ml, penumbral volume ≥15ml, and a total hypoperfused volume:core volume ratio >1.8. The primary outcome was good functional outcome at 90 days, defined as a modified Rankin Scale (mRS) score 0-2. follow-up infarct volume, core expansion and penumbral salvage volumes were secondary outcomes. Of 572 anterior circulation EVT patients, CTP source image data required to generate objective maps were available in 170, and a Target Mismatch was present in 151 (89%). The rate of 90-day good functional outcome was similar between Target Mismatch (53%) and Large Core Non-Mismatch groups (46%, p = 0.629). Median follow-up infarct volume in the Large Core Non-Mismatch group (104ml [IQR 25ml-189ml]) was larger than that in the Target Mismatch patients (16ml [8ml-47ml], p<0.001). Despite a lack of formal CTP selection criteria, the majority of patients treated at our centres had a Target Mismatch. Patients without Target Mismatch had larger follow-up infarct volumes, but the functional recovery rate was similar to that in Target Mismatch patients. Infarct volumes should be included as objective assessment criteria in the evaluation of the efficacy of EVT in non-Target Mismatch patients.
| S-EPMC10501580 | biostudies-literature
Project description:BackgroundRecent randomized trials have suggested that endovascular thrombectomy (EVT) alone may provide similar functional outcomes as the current standard of care, EVT combined with intravenous alteplase treatment, for acute ischemic stroke secondary to large vessel occlusion. We conducted an economic evaluation of these 2 therapeutic options.MethodsWe constructed a decision analytic model with a hypothetical cohort of 1000 patients to assess the cost-effectiveness of EVT with intravenous alteplase treatment versus EVT alone for acute ischemic stroke secondary to large vessel occlusion from both the societal and public health care payer perspectives. We used studies and data published in 2009-2021 for model inputs, and acquired cost data for Canada and China, representing high- and middle-income countries, respectively. We calculated incremental cost-effectiveness ratios (ICERs) using a lifetime horizon and accounted for uncertainty using 1-way and probabilistic sensitivity analyses. All costs are reported in 2021 Canadian dollars.ResultsIn Canada, the difference in quality-adjusted life-years (QALYs) gained between EVT with alteplase and EVT alone was 0.10 from both the societal and health care payer perspectives. The difference in cost was $2847 from a societal perspective and $2767 from the payer perspective. In China, the difference in QALYs gained was 0.07 from both perspectives, and the difference in cost was $1550 from the societal perspective and $1607 from the payer perspective. One-way sensitivity analyses showed that the distributions of modified Rankin Scale scores at 90 days after stroke were the most influential factor on ICERs. For Canada, compared to EVT alone, the probability that EVT with alteplase would be cost-effective at a willingness-to-pay threshold of $50 000 per QALY gained was 58.7% from a societal perspective and 58.4% from a payer perspective. The corresponding values for at a willingness-to-pay threshold of $47 185 (3 times the Chinese gross domestic product per capita in 2021) were 65.2% and 67.4%.InterpretationFor patients with acute ischemic stroke due to large vessel occlusion eligible for immediate treatment with both EVT alone and EVT with intravenous alteplase treatment, it is uncertain whether EVT with alteplase is cost-effective compared to EVT alone in Canada and China.
| S-EPMC10205869 | biostudies-literature
Project description:Background A target mismatch profile can identify good clinical response to recanalization after acute ischemic stroke, but does not consider region specificities. Purpose To test whether location-weighted infarction core and mismatch, determined from diffusion and perfusion MRI performed in patients with acute stroke, could improve prediction of good clinical response to mechanical thrombectomy compared with a target mismatch profile. Materials and Methods In this secondary analysis, two prospectively collected independent stroke data sets (2012-2015 and 2017-2019) were analyzed. From the brain before stroke (BBS) study data (data set 1), an eloquent map was computed through voxel-wise associations between the infarction core (based on diffusion MRI on days 1-3 following stroke) and National Institutes of Health Stroke Scale (NIHSS) score. The French acute multimodal imaging to select patients for mechanical thrombectomy (FRAME) data (data set 2) consisted of large vessel occlusion-related acute ischemic stroke successfully recanalized. From acute MRI studies (performed on arrival, prior to thrombectomy) in data set 2, target mismatch and eloquent (vs noneloquent) infarction core and mismatch were computed from the intersection of diffusion- and perfusion-detected lesions with the coregistered eloquent map. Associations of these imaging metrics with early neurologic improvement were tested in multivariable regression models, and areas under the receiver operating characteristic curve (AUCs) were compared. Results Data sets 1 and 2 included 321 (median age, 69 years [IQR, 58-80 years]; 207 men) and 173 (median age, 74 years [IQR, 65-82 years]; 90 women) patients, respectively. Eloquent mismatch was positively and independently associated with good clinical response (odds ratio [OR], 1.14; 95% CI: 1.02, 1.27; P = .02) and eloquent infarction core was negatively associated with good response (OR, 0.85; 95% CI: 0.77, 0.95; P = .004), while noneloquent mismatch was not associated with good response (OR, 1.03; 95% CI: 0.98, 1.07; P = .20). Moreover, adding eloquent metrics improved the prediction accuracy (AUC, 0.73; 95% CI: 0.65, 0.81) compared with clinical variables alone (AUC, 0.65; 95% CI: 0.56, 0.73; P = .01) or a target mismatch profile (AUC, 0.67; 95% CI: 0.59, 0.76; P = .03). Conclusion Location-weighted infarction core and mismatch on diffusion and perfusion MRI scans improved the identification of patients with acute stroke who would benefit from mechanical thrombectomy compared with the volume-based target mismatch profile. Clinical trial registration no. NCT03045146 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Nael in this issue.
| S-EPMC9885343 | biostudies-literature
Project description:The perfusion-/diffusion-weighted imaging (PWI/DWI) mismatch and the diffusion/fluid attenuated inversion recovery (DWI/FLAIR) mismatch are magnetic resonance imaging (MRI) markers of evolving brain ischemia. We examined whether the DWI/FLAIR mismatch was independently associated with the PWI/DWI mismatch. Furthermore, we determined whether the presence of the DWI/FLAIR mismatch in patients with the PWI/DWI mismatch would provide additional information regarding last seen normal time (LTM). We used data from the 'AX200 for ischemic stroke' trial (AXIS 2 study NCT00927836). We studied the association between the presence of the DWI/FLAIR and PWI/DWI mismatch, baseline National Institute of Health Stroke Scale (NIHSS), age, ischemic-core volume, gender, intravenous (IV) tissue plasminogen activator (tPA), and perfusion-mismatch volume in univariate analysis. Significant variables (P<0.05) were added into the final multivariate model. We analyzed 197 patients. Seventy-two (37%) had both the PWI/DWI and the DWI/FLAIR mismatch. Patients with the double mismatch pattern had a shorter LTM than patients with the PWI/DWI mismatch alone (Median difference 90 minutes, P<0.01). Multivariate analysis confirmed the independent association between the two mismatch patterns (odds ratio (OR) 2.6, 95% confidence interval (CI) 1.2 to 5.4). Our study implies that the DWI/FLAIR mismatch and PWI/DWI mismatch are strongly associated, independent from LTM. Furthermore, in the presence of the PWI/DWI mismatch, the DWI/FLAIR pattern indicates a shorter LTM. This could have implications in selecting patients for reperfusion therapy.
| S-EPMC4640316 | biostudies-literature
Project description:Diffusion-weighted magnetic resonance imaging (DW MRI) is a fast unenhanced technique that shows promise as a stand-alone modality for cancer screening and characterization. Currently, DW MRI may have lower sensitivity than that of dynamic contrast-enhanced MRI as a standalone modality for breast cancer detection but superior to that of mammography, which may provide a useful alternative for supplemental screening. Standardized acquisition and interpretation of DW MRI can improve the image quality and reduce the variability of the results. Furthermore, high-resolution DW MRI, with advanced techniques and postprocessing, will facilitate better detection and characterization of subcentimeter cancers and reduce false-negatives and false-positives. Future results from ongoing prospective multicenter clinical trials using standardized and optimized protocols will facilitate the use of DW MRI as a stand-alone modality.
| S-EPMC9432391 | biostudies-literature
Project description:Background and purposeThe absence of acute ischemic lesions in diffusion-weighted imaging (DWI) in transient ischemic attack (TIA) patients makes it difficult to diagnose the true vascular etiologies. Among patients with DWI-negative TIA, we investigated whether the presence of a perfusion-weighted imaging (PWI) abnormality implied a true vascular event by identifying new acute ischemic lesions in follow-up magnetic resonance imaging (MRI) in areas corresponding to the initial PWI abnormality.MethodsThe included patients underwent DWI and PWI within 72 hours of TIA and also follow-up DWI at 3 days after the initial MRI. These patients had visited the emergency room between July 2009 and May 2015. Patients who demonstrated initial DWI lesions were excluded. The initial PWI abnormalities in the corresponding vascular territory were visually classified into three patterns: no abnormality, focal abnormality, and territorial abnormality.ResultsNo DWI lesions were evident in initial MRI in 345 of the 443 TIA patients. Follow-up DWI was applied to 87 of these 345 DWI-negative TIA patients. Initial PWI abnormalities were significantly associated with follow-up DWI abnormalities: 8 of 43 patients with no PWI abnormalities (18.6%) had new ischemic lesions, whereas 13 of 16 patients with focal perfusion abnormalities (81.2%) had new ischemic lesions in the areas of initial PWI abnormalities [odds ratio (OR)=15.1, 95% confidence interval (CI)=3.6-62.9], and 14 of 28 patients with territorial perfusion abnormalities (50%) had new lesions (OR=3.7, 95% CI=1.2-11.5).ConclusionsPWI is useful in defining whether or not the transient neurological symptoms in DWI-negative TIA are true vascular events, and will help to improve the understanding of the pathomechanism of TIA.
| S-EPMC5392454 | biostudies-literature
Project description:We aimed to develop and validate a multiparametric MR radiomics model using conventional, diffusion-, and perfusion-weighted MR imaging for better prognostication in patients with newly diagnosed glioblastoma. A total of 216 patients with newly diagnosed glioblastoma were enrolled from two tertiary medical centers and divided into training (n = 158) and external validation sets (n = 58). Radiomic features were extracted from contrast-enhanced T1-weighted imaging, fluid-attenuated inversion recovery, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. After radiomic feature selection using LASSO regression, an individualized radiomic score was calculated. A multiparametric MR prognostic model was built using the radiomic score and clinical predictors. The results showed that the multiparametric MR prognostic model (radiomics score + clinical predictors) exhibited good discrimination (C-index, 0.74) and performed better than a conventional MR radiomics model (C-index, 0.65, P < 0.0001) or clinical predictors (C-index, 0.66; P < 0.0001). The multiparametric MR prognostic model also showed robustness in external validation (C-index, 0.70). Our results indicate that the incorporation of diffusion- and perfusion-weighted MR imaging into an MR radiomics model to improve prognostication in glioblastoma patients improved its performance over that achievable using clinical predictors alone.
| S-EPMC7060336 | biostudies-literature
Project description:ObjectivesWe aimed to develop machine learning (ML) models based on diffusion- and perfusion-weighted imaging fusion (DP fusion) for identifying stroke within 4.5 h, to compare them with DWI- and/or PWI-based ML models, and to construct an automatic segmentation-classification model and compare with manual labeling methods.MethodsML models were developed from multimodal MRI datasets of acute stroke patients within 24 h of clear symptom onset from two centers. The processes included manual segmentation, registration, DP fusion, feature extraction, and model establishment (logistic regression (LR) and support vector machine (SVM)). A segmentation-classification model (X-Net) was proposed for automatically identifying stroke within 4.5 h. The area under the receiver operating characteristic curve (AUC), sensitivity, Dice coefficients, decision curve analysis, and calibration curves were used to evaluate model performance.ResultsA total of 418 patients (≤ 4.5 h: 214; > 4.5 h: 204) were evaluated. The DP fusion model achieved the highest AUC in identifying the onset time in the training (LR: 0.95; SVM: 0.92) and test sets (LR: 0.91; SVM: 0.90). The DP fusion-LR model displayed consistent positive and greater net benefits than other models across a broad range of risk thresholds. The calibration curve demonstrated the good calibration of the DP fusion-LR model (average absolute error: 0.049). The X-Net model obtained the highest Dice coefficients (DWI: 0.81; Tmax: 0.83) and achieved similar performance to manual labeling (AUC: 0.84).ConclusionsThe automatic segmentation-classification models based on DWI and PWI fusion images had high performance in identifying stroke within 4.5 h.Clinical relevance statementPerfusion-weighted imaging (PWI) fusion images had high performance in identifying stroke within 4.5 h. The automatic segmentation-classification models based on DWI and PWI fusion images could provide clinicians with decision-making guidance for acute stroke patients with unknown onset time.Key points• The diffusion/perfusion-weighted imaging fusion model had the best performance in identifying stroke within 4.5 h. • The X-Net model had the highest Dice and achieved performance close to manual labeling in segmenting lesions of acute stroke. • The automatic segmentation-classification model based on DP fusion images performed well in identifying stroke within 4.5 h.
| S-EPMC11399198 | biostudies-literature
Project description:Data on infarcts in new territory (INT) in patients undergoing endovascular stroke treatment for acute large-vessel occlusions are sparse. Aim of this study was to assess the prevalence, risk factors, and clinical relevance of INT. For this purpose, all patients in a single-center prospective registry who underwent endovascular stroke treatment and received pre- and post-interventional diffusion-weighted imaging were included (N = 259). Using an established scoring system, INT were classified according to size (I-III, ≤2 mm, >2 mm ≤20 mm, >20 mm) and likelihood of being related to the intervention (A, high likelihood; B, low likelihood). Additionally, a new type of infarct, that occurred in a territory distal to the occlusion, but was initially not hypoperfused, was defined as an infarct in initially not hypoperfused territory (IINHT). A total of 180 INT and 38 IINHT were observed in 32.8% (N = 85/259) of patients. In most patients, INT were angiographically occult (90.2%), and 13 patients had INT/IINHT larger than 2 cm (type III). Absence of protection during stent-retrieval and a cardio-embolic stroke origin were associated with higher incidence of INT/IINHT, whereas pretreatment with IV tPA showed no association, even when different bolus timing was considered. INT/IINHT were associated with lower rates of functional independence with increasing size type after adjusting for confounders (adjusted Odds Ratio per size group increase 0.63, 95% confidence interval 0.46-0.86). In conclusion, INT and IINHT are not rare, are associated with poor outcome with increasing size, and they may serve as a surrogate endpoint for safety evaluation of new devices and endovascular techniques. Further research on associated factors is warranted.
| S-EPMC7239894 | biostudies-literature
Project description:The presence of a diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch holds potential value in identifying candidates for recanalization treatment. However, the visual assessment of DWI-FLAIR mismatch is subject to limitations due to variability among raters, which affects accuracy and consistency. To overcome these challenges, we aimed to develop and validate a deep learning-based classifier to categorize the mismatch. We screened consecutive acute ischemic stroke patients who underwent DWI and FLAIR imaging from a four stroke centers. Two centers were used for model development and internal testing (derivation cohort), while two independent centers served as external validation cohorts. We developed Convolutional Neural Network-based classifiers for two binary classifications: DWI-FLAIR match versus non-match (Label Set I) and match versus mismatch (Label Set II). A total of 2369 patients from the derivation set and 679 patients from two external validation sets (350 and 329 patients) were included in the analysis. For Label Set I, the internal test set AUC was 0.862 (95% CI 0.841-0.884, with external validation AUCs of 0.829 (0.785-0.873) and 0.835 (0.790-0.879). Label Set II showed higher performance with internal test AUC of 0.934 (0.911-0.957) and external validation AUCs of 0.883 (0.829-0.938) and 0.913 (0.876-0.951). A deep learning-based classifier for the DWI-FLAIR mismatch can be used to diminish subjectivity and support targeted decision-making in the treatment of acute stroke patients.
| S-EPMC11836310 | biostudies-literature