Early Severe Midline Shift in Acute Ischemic Stroke Following Endovascular Thrombectomy Within 24 Hours

Early Severe Midline Shift in Acute Ischemic Stroke Following Endovascular Thrombectomy Within 24 Hours | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Early Severe Midline Shift in Acute Ischemic Stroke Following Endovascular Thrombectomy Within 24 Hours Nannan Han, Xiaobo Zhang, Yu Zhang, Leshi Zhang, Haojun Ma, Tengfei Li, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7191348/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Neuroradiology → Version 1 posted You are reading this latest preprint version Abstract Purpose: Early and severe (ES) midline shift (MLS ≥10 mm) simultaneously occurring within 24 hours after endovascular thrombectomy (EVT) is a life-threatening emergency that requires immediate intervention. This study aims to describe ES-MLS and develop a predictive model in anterior circulation occlusion who have undergone EVT. Methods: This retrospective cohort study utilized data from a prospective registry. Functional outcome was defined as a modified Rankin Scale score of 0–2. Radiomic features extracted from pre-EVT diffusion-weighted imaging were subjected to LASSO regression with fourfold cross-validation. Clinical features were selected via multivariable regression and integrated into a nomogram, with performance evaluated through receiver operating characteristic curve analysis in both training and validation datasets. Results: A total of 481 patients (median age 68 [IQR 58–76], 39.7% female) were included in this study, which consisted of a training dataset (n = 361) and a validation dataset (n = 120). In the ES-MLS group, 85.7% had died and none had a functional outcome at the 90-day follow-up. Recanalization, NIHSS score, and two radiomic features were identified as factors associated with ES-MLS in the nomogram. The predictive model exhibited an area under the curve (AUC) of 0.844 (95% confidence interval [CI], 0.803–0.880) in the training dataset and 0.823 (95% CI, 0.743–0.887) in the validation dataset. Conclusion: This is the initial structured overview of ES-MLS after EVT, featuring a model designed for personalized prediction of ES-MLS. The tool may enhance patient selection before EVT and refine the aggressive monitoring strategy after EVT. midline shift thrombectomy malignant cerebral edema diffusion-weighted imaging radiomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke remains a significant cause of morbidity and mortality worldwide. 1 Over the past decade, endovascular thrombectomy (EVT) has emerged as a critical therapeutic method, demonstrating significant efficacy in improving clinical outcomes through arterial recanalization. 2 Expedited EVT implementation is particularly vital for patients with large ischemic cores, as timely recanalization may lead to a remarkable functional outcome. 3 – 8 However, despite the reperfusion achieved by EVT, midline shift (MLS) remains a formidable challenge in the post-EVT period. 9 MLS, often indicative of substantial cerebral edema and increased intracranial pressure, significantly increases the risk of poor outcomes. 10 , 11 The pathophysiology of MLS involves ischemia-induced capillary dysfunction, which initiates a cascade of ionic imbalance and inflammatory damage. These processes collectively drive cerebral edema, tissue swelling, and functional impairment. 12 Both the severity and early occurrence of midline shift (MLS) are associated with poor prognosis. In clinical practice, patients with early and severe (ES) MLS, simultaneously detected by follow-up computed tomography (CT) scans within 24 hours after EVT, often experience poor outcomes. Nevertheless, reliable neurological monitoring for ES-MLS remains clinically challenging due to postoperative sedation requirements and severe baseline neurological deficits before EVT. Early prediction of ES-MLS has become a critical priority in guiding time-sensitive therapeutic interventions. Because of delayed intervention in patients showing signs of cerebral herniation before decompressive hemicraniectomy increased risk of unfavorable outcomes. 13 To our knowledge, no studies currently systematically describe and predict ES-MLS. Since the positive results of the RCT with a large ischemic core, the opportunity for these patients to undergo EVT has been increasing. Most of the large ischemic core studies have included diffusion-weighted imaging (DWI) in the preoperative evaluation method, including the published TENSION 3 , ANGEL-ASPECT 5 , SELECT2 6 , LASTE 7 . Among these, the RESCUE-Japan LIMIT study 8 reported that 88 out of 101 patients in the EVT group and 87 out of 102 patients in the non-EVT group were evaluated by DWI. By integrating quantitative radiomic features extracted from pre-EVT DWI with clinical features, we aim to describe ES-MLS and construct an intuitive risk stratification tool that enables clinicians to identify high-risk individuals. For the analysis of RFs, quality control was performed using the intraclass correlation coefficient (ICC), with RFs exhibiting ICC values lower than 0.75 being excluded. The remaining RFs were selected using the least absolute shrinkage and selection operator (LASSO) in the training cohort. A nomogram was constructed based on the selected CFs and RFs, and its discriminative ability was evaluated using a separate validation dataset. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the nomogram's performance, while calibration plots were employed to examine the agreement between actual and nomogram-predicted ES-MLS values. The ICC analysis was conducted using Python 3.9.7 and the Pinguin 0.5.3 package, ROC analysis was performed using MedCalc 19.2.0, and RF extraction utilized Pyradiomics 3.1.0, Scikit-learn 0.24.2, Pandas 2.1.4, and NumPy 1.26.2 packages. Additional statistical analyses were carried out using SPSS (version 25.0, IBM, NY, USA) and R statistical software (version 4.3.0, R Foundation for Statistical Computing, Vienna, Austria). Methods Patients Characteristics The patients in this study were recruited from an ongoing prospective cohort study (xxxxxx). We conducted a retrospective analysis of this ongoing study, enrolling cases from July 2018 to July 2024. The study participants were selected based on the following inclusion criteria: (1) thrombectomy procedures performed according to established standards of care, (2) pre-thrombectomy DWI was performed, (3) onset-to-DWI time within 24 hours, (4) presence of large vessel occlusion in the anterior circulation (including the internal carotid artery and M1-M2 segments of the middle cerebral artery) confirmed by DSA, and (5) availability of 90-day mRS follow-up (0–6, with higher scores indicating worse prognosis). The exclusion criteria were: (1) prior history of stroke with an mRS score of 3 to 5, (2) CT or MRI before thrombectomy showing brain tissue swelling with midline shift, (3) known allergies to contrast agents (more severe than skin rash), (4) evidence of acute intracranial hemorrhage on CT or MRI before thrombectomy, (5) high risk of hemorrhage (platelet count < 40,000/uL), (6) DWI motion artifacts that hinder accurate identification of the infarcted area, and (7) simultaneous occlusion in both anterior and posterior circulations (Fig. 1 ). This study was approved by the Institutional Review Board of the Affiliated Hospital of Northwest University (xxxxxx), and written informed consent was obtained from the legally authorized representatives for EVT. Clinical data collection The demographic and clinical characteristics of the patients were analyzed, including age, gender, medical history, and ischemic stroke severity, assessed using the NIHSS score in the emergency department. Other recorded parameters included the side and site of the occlusion, time from stroke onset to DWI, time from DWI to reperfusion, presence of wake-up stroke (calculated as the midpoint between the last normal time and the time symptoms were detected), 14 and administration of intravenous recombinant tissue plasminogen activator (rt-PA) or recombinant human TNK tissue-type plasminogen activator (TNK). For comparison between the training and validation sets, additional parameters were recorded, including infarction volume, time from DSA to follow-up CT, stroke etiology, parenchymal hemorrhage type 2, 15 and reperfusion status according to the modified thrombolysis in cerebral infarction (mTICI) grading system. A functional outcome was defined as a modified Rankin Scale (mRS) score of 0 to 2, a fair outcome as an mRS of 0 to 3, and death (mRS score of 6) was assessed at 90 ± 7 days after thrombectomy via telephone or face-to-face interview with the patient or their relatives, conducted by neurologists. Study outcomes The primary outcome, ES-MLS, was defined based on a combination of clinical and neuroimaging criteria 16 : (1) Neuroimaging evidence of a midline shift ≥ 10 mm on follow-up CT within 24 hours after EVT; and (2) Clinical signs of unilateral to bilateral pupil dilation within 24 hours after thrombectomy when CT was not performed in 6 cases. MLS ≥ 10 mm was chosen because it is the imaging critical value and surgical treatment is advocated for this condition. 17 A diagnosis of ES-MLS requires the presence of at least one neuroimaging criterion and one clinical criterion. Examples of the ES-MLS flow are shown in Fig. 2 . Image Acquisition DWI scans were acquired on a PHILIPS Ingenia 3.0T MRI scanner using the following parameters: repetition time (TR) of 2506 ms, echo time (TE) of 68 ms, field of view (FOV) of 230 mm × 230 mm, image matrix size of 152 × 102 pixels, b-values of 0 and 1000 s/mm², slice thickness of 5.0 mm, and an interslice gap of 1.0 mm. The DWI data were automatically uploaded to the Picture Archiving and Communication System (PACS), subsequently downloaded in DICOM format, and sent for evaluation by two independent readers in the laboratory. Two neurologists manually segmented the regions with hyperintense signals in the DWI using 3D Slicer software 18 (version 4.11.2 to 5.2.2). The neurologists were blinded to the thrombectomy procedure results and clinical outcomes. Radiomic features (RFs) 19 were automatically extracted from the segmented hyperintense signal regions using Pyradiomics (version 3.1.0). A total of 107 features were derived from the DWI data, including 18 first-order statistics, 14 shape-based features, 25 features from the Gray Level Co-occurrence Matrix (GLCM), 13 from the Gray Level Dependence Matrix (GLDM), 16 from the Gray Level Run Length Matrix (GLRLM), 16 from the Gray Level Size Zone Matrix (GLSZM), and 5 from the Neighboring Gray Tone Difference Matrix (NGTDM). Follow-up CT scans were performed 24 hours after EVT unless the patients presented with clinical symptoms such as anisocoria. The CT scans were acquired using a SIEMENS 128-slice scanner with the following parameters: 120 kV, automatic mAs, field of view (FOV) of 200 mm, matrix size of 512 × 512, and slice thickness of 5 mm, or a GE 64-slice scanner with similar parameters: 120 kV, automatic mAs, FOV of 250 mm, matrix size of 512×512, and slice thickness of 5 mm. The images were saved in DICOM format and sent to the imaging laboratory for further analysis. For the follow-up CT images, reformatting was performed using 3D Slicer to align the bilateral pupils in the same horizontal plane. The maximum slice was selected at the point of maximal deviation from the line drawn between the anterior and posterior attachments of the falx cerebri (Figure S1 ). 20 Statistical Analysis To analyze the clinical features (CFs), the normality of the data distributions was assessed using histograms and the Shapiro-Wilk test. A p-value larger than 0.05 was considered indicative of a normal distribution. Quantitative variables are expressed as medians with interquartile range (IQR). Bivariate comparisons between CFs were conducted using the χ² test for categorical variables and the Mann-Whitney U test for quantitative variables. Missing data were rare (< 5%) and was not imputed. In the univariate logistic regression analysis, variables with p-values less than 0.1 were further evaluated in a multivariable logistic regression analysis using a backward selection approach. Clinical features with p-values less than 0.05 in the multivariable analysis were deemed statistically significant. Results From July 2018 to July 2024, a total of 481 patients were enrolled, with 49 (10.2%) experiencing ES-MLS within 24 hours after thrombectomy. A total of 744 patients undergoing thrombectomy were screened for inclusion; however, 138 (18.5%) were excluded due to posterior circulation artery occlusion (including 2 cases of simultaneous anterior and posterior circulation occlusion), 68 (9.1%) were excluded due to the absence of DWI, and 43 (5.8%) were excluded because the time of symptoms onset to DWI acquisition was more than 24 hours. Additionally, 8 patients (1.1%) with poor-quality DWI images, 3 patients (0.4%) with anterior cerebral artery occlusion, and 3 patients (0.4%) with a history of mRS scores more than 2 were excluded. As shown in Table 1 , the median age of the included patients was 68 years (IQR 58.0–76.0), and 191 (39.7%) of the patients were female. The median baseline NIHSS score was 13 (IQR 9–17). At the 90-day follow-up, 52.8% of patients had an mRS score of 0 to 2, and 17.9% had died. Patients with ES-MLS exhibited a higher proportion of atrial fibrillation, internal carotid artery occlusion, a higher baseline NIHSS score, larger infarction volume, and higher proportion of all type hemorrhage and Parenchymal hemorrhage type 2. Patients with ES-MLS also had poorer outcomes, including a lower proportion of mRS 0 to 2 (0 [0%] vs. 254 [58.8%], p < 0.001), mRS 0 to 3 (1 [2.0%] vs. 298 [69.0%], p < 0.001), and 85.7% had died. Table 1 Demographics and characteristics of the Non-ES-MLS and ES-MLS groups. Variable All patients (N = 481) Non-ES-MLS group ( N = 432 ) ES-MLS group ( N = 49 ) p Value mRS 0–2, n. (%) 254 (52.8) 254 (58.8) 0 (0) < 0.001 mRS 0–3, n. (%) 299 (62.2) 298 (69.0) 1 (2.0) < 0.001 mRS 6, n. (%) 86 (17.9) 44 (10.2) 42 (85.7) < 0.001 Age, y, median (IQR) 68 (58.0–76.0) 68 (58.0–76.0) 70 (55.0–78.0) 0.728 Female sex, n. (%) 191 (39.7) 167 (38.7) 24 (49.0) 0.162 Previous stroke, n. (%) 93 (19.3) 81 (18.8) 12 (24.5) 0.335 Hypertension, n. (%) 280 (58.2) 246 (56.9) 34 (69.4) 0.094 Diabetes mellitus, n. (%) 88 (18.3) 79 (18.3) 9 (18.4) 0.989 Hyperlipidemia, n. (%) 108 (22.5) 99 (22.9) 9 (18.4) 0.470 Atrial fibrillation, n. (%) 165 (34.3) 141 (32.6) 24 (49.0) 0.022 Smoking, n. (%) 169 (35.1) 152 (35.2) 17 (34.7) 0.946 NIHSS score, median (IQR) 13 (9.0–17.0) 13.0 (8.0–17.0) 16.0 (13.5–21.0) < 0.001 Occlusion site, n. (%) Internal carotid artery 216 (44.9) 187 (43.3) 29(59.2) 0.034 M1 segment of MCA 189 (39.3) 174 (40.3) 15 (30.6) 0.189 M2 segment of MCA 76 (15.8) 71 (16.5) 5 (10.2) 0.255 Side, n. (%) 254 (52.8) 232 (53.7) 22 (44.9) 0.242 Infarction volume, mm 3 , median (IQR) 14.5 (5.7–54.3) 12.8 (5.3–43.1) 119.7 (28.4-185.8) < 0.001 Time from onset to DWI, min, median (IQR) 295.5 (161.0-508.8) 296.0 (160.3-510.8) 283.5 (162.8-483.3) 0.374 Time from DWI to reperfusion, min, median(IQR) 148.0 (119.0-188.75) 148.0 (119.0-186.0) 158.0 (102.0-203.5) 0.928 Time from DSA to follow-up CT, hour, median (IQR) & 21.1 (17.6–24.8) 21.1(17.4–25.0) 21.3 (18.1–24.7) 0.936 Wake-up stroke, n. (%) 119 (24.7) 111 (25.7) 8 (16.3) 0.150 Intravenous rt-PA or TNK 163 (33.9) 142 (32.9) 21 (42.9) 0.162 Cause of stroke, n(%) Embolic 256 (53.2) 224 (51.9) 32 (65.3) 0.074 Atherosclerotic 200 (41.6) 186 (43.1) 14 (28.6) 0.051 Others 25 (5.2) 22 (5.1) 3 (6.1) 0.758 Hemorrhage all type & 147 (30.9) 110 (25.6) 37 (82.2) < 0.001 Parenchymal hemorrhage type 2 & 32 (6.7) 19 (3.0) 13 (42.2) < 0.001 mTICI 2b-c, n.(%) 429 (89.2) 389 (90.0) 40 (81.6) 0.072 ES-MLS, early severe midline shift; mRS, modified Rankin Scale; IQR, interquartile range; NIHSS, national institute of the health stroke scale; MCA, middle cerebral artery; DWI, diffusion weighted imaging; DSA, digital subtraction angiography; CT, computed tomography; rt-PA, intravenous recombinant tissue plasminogen activator; TNK, recombinant human TNK tissue-type plasminogen activator; mTICI, modified treatment in cerebral ischemia; & Six patients missed a CT scan after thrombectomy within 24 hours, and the analysis was conducted using the actual values. After randomization, the patients in the training and validation sets were well-matched, with the exception of wake-up stroke (81 [22.4%] vs. 38 [31.7%], p = 0.042) and reperfusion status (316 [87.5%] vs. 113 [94.2%], p = 0.043). No significant differences were noted in medical history, ischemic stroke severity, or other recorded parameters (Table S1 ). The results of the logistic regression analysis for ES-MLS in the training dataset are in Table 2 . The univariate analysis indicated that atrial fibrillation, baseline NIHSS score, and mTICI status were potential predictors of ES-MLS (P < 0.1). The multivariate analysis revealed that NIHSS score (odds ratio [OR], 1.103; 95% confidence interval [CI], 1.042–1.168; P = 0.001), and mTICI (OR, 0.336; 95% CI, 0.136–0.828; P = 0.018) were significant predictors of ES-MLS. Additionally, 107 RFs were extracted from the segmented regions of two readers, and 73 RFs with an ICC greater than 0.75 were selected for further analysis. Subsequently, LASSO analysis was performed, and two RFs, gldm_DependenceNonUniformity and shape_MeshVolume, were selected as significant predictors of the ES-MLS (Figure S2 ). Table 2 Logistic regression analysis of the risk factors associated with ES-MLS in the training cohort. Variable Unadjusted OR (95% CI) p Value adjusted OR (95% CI) p Value Age, y 1.003(0.975–1.030) 0.856 Sex 1.681(0.835–3.382) 0.146 Previous stroke 1.213(0.526–2.796) 0.650 Hypertension 1.151(0.560–2.367) 0.701 Diabetes mellitus 1.042(0.413–2.631) 0.930 Hyperlipidemia 0.558(0.209–1.488) 0.244 Atrial fibrillation 2.045(1.013–4.129) 0.046 1.652(0.785–3.478) 0.186 Smoking 0.830(0.392–1.754) 0.625 NIHSS score 1.105(1.046–1.167) < 0.001 1.103(1.042–1.168) 0.001 Occlusion site Internal carotid artery 1.680(0.834–3.385) 0.147 M1 segment of MCA 0.748(0.360–1.554) 0.436 M2 segment of MCA 0.607(0.206–1.786) 0.364 Side 0.631(0.312–1.277) 0.200 Time from onset to DWI, min 0.999(0.998–1.001) 0.342 Time from DWI to reperfusion, min 0.999(0.995–1.004) 0.707 mTICI 2b-c 0.432(0.183–1.021) 0.056 0.336(0.136–0.828) 0.018 Embolic 1.759(0.847–3.653) 0.130 Wake-up stroke 0.548(0.206–1.462) 0.230 Intravenous rt-PA/TNK 0.983(0.472–2.050) 0.964 ES-MLS, early severe midline shift; OR, odd ratio; CI, confidence interval; NIHSS, ational institute of the health stroke scale; MCA, middle cerebral artery; DWI: diffusion weighted imaging; mTICI, modified treatment in cerebral ischemia; rt-PA, intravenous recombinant tissue plasminogen activator; TNK, recombinant human TNK tissue-type plasminogen activator. The results indicated that higher total points, as calculated by summing the assigned number of points for each predictor in the nomogram, corresponded to an increased risk of unfavorable outcomes (Fig. 3 ). The performance of the nomogram was assessed by the AUC of ROC, which was found to be 0.844 (95% CI, 0.803–0.880, p < 0.001) in the training dataset and 0.823 (95% CI, 0.743–0.887, p < 0.001) in the validation dataset, indicating good predictive power (Fig. 4 ). The calibration plot was used to compare the prediction of ES-MLS made by the nomogram with actual observations (Figure S3 ). Discussion In this study, we aimed to describe an uncommon yet potentially fatal complication, ES-MLS, and to develop an imaging-clinical predictive model for its early detection following EVT. Our single-center study found a 10.2% incidence of ES-MLS in this cohort. Remarkably, 85.7% of patients with ES-MLS ≥ 10 mm after EVT had died within 90-day, and all affected individuals experienced poor clinical outcomes. The radiomic-clinical nomogram developed, which incorporates the NIHSS score, recanalization status, and two radiomic features exhibited good predictive performance, with an AUC of 0.844 in the training dataset and 0.823 in the validation dataset. This predictive tool provides clinicians with an intuitive instrument for early risk stratification and therapeutic decision-making. Animal studies consistently show that ischemia-reperfusion injury promotes cerebral edema. 21 , 22 However, the effect of EVT on the progression of cerebral edema remains controversial. Broocks et al. 23 suggested that EVT improves outcomes by reducing cerebral edema, with an estimated mediation proportion of 66%. In contrast, a secondary analysis 24 of the ANGEL-ASPECT trial revealed that EVT increased MLS in patients with a large ischemic core, with MLS > 5mm occurring in 26.9% of the EVT group compared to 15.2% in the non-EVT group (adjusted odds ratio: 2.61) at 24-hour follow-up. This phenomenon partially mediated the poor outcomes after EVT (mediation proportion: -25%; 95% CI: -46.54 to -4.10). The controversy can be explained from three perspectives: 1. The size of the infarct core affects MLS: For patients with small infarct cores, although EVT-induced reperfusion may lead to increased edema in the infarcted tissue, but edema is insufficient to cause MLS. In contrast, patients with small infarcts who do not undergo EVT, the lack of reperfusion causes further enlargement of the infarct core over time, leading to MLS. 2. Large infarcts with DWI high signal and reperfusion promote MLS: DWI imaging showing extensive high signals in large infarcts indicates limited salvageable brain tissue. Reperfusion following EVT induces transient post-EVT edema. 25 This reperfusion injury exacerbates the disruption of the blood-brain barrier, increasing vascular permeability and leading to vasogenic edema. 9 Studies have shown that patients with infarct volumes greater than 130 ml may experience MLS following EVT, suggesting that reperfusion treatment may have a detrimental effect in these cases 9 . 3. Large infarcts with DWI low signal and reperfusion affect MLS: In early-stage ischemic stroke patients, DWI imaging reveals a low signal and short-term reperfusion can restore blood flow, preventing the transition from low to high signal intensity. Furthermore, the administration of anti-edema drugs following reperfusion may more effectively target the edematous regions, reducing edema and slowing the progression of MLS. In contrast, patients without reperfusion may experience significant tissue damage, leading to persistent edema due to cellular death. 25 The current investigation identified NIHSS score and recanalization status (mTICI) as clinical determinants of ES-MLS, consistent with malignant middle cerebral artery infarction within 5 days after EVT. 26 The radiomic parameter shape_MeshVolume emerged as the most significant imaging predictor derived from pre-EVT DWI sequences. The volume is calculated from the triangle mesh of the region of interest, which quantifies ischemic lesion topology through three-dimensional surface mesh decomposition 19 , and is highly correlated with infarct volume. The second critical radiomic feature, gldm_DependenceNonUniformity (DN), indicates that a higher DN value reflects significant variation in pixel dependencies, resulting in an uneven distribution of tissue density and gray levels. The greater the signal difference between high-signal DWI lesions and normal brain tissue, the more severe the brain injury. Limitations This investigation has several limitations. First, the single-center retrospective design introduces inherent selection biases and limits generalizability. Second, our predictive model specifically focused on preoperative variables at the time of EVT procedure finish, excluding postoperative monitoring variables such as Glasgow Coma Scale scores and changes in NIHSS after EVT. Third, the radiomic analysis chooses first-order features to consider the clinical interpretability. This approach mitigated overfitting risks associated with higher-order texture parameters. Fourth, manual infarct segmentation remains labor-intensive, which poses challenges for real-time clinical implementation. Emerging artificial intelligence-driven segmentation algorithms could enhance both efficiency and reproducibility in future applications. Finally, perfusion images from MR imaging were not considered in this study. Conclusions These findings emphasize the importance of early detection of ES-MLS and may contribute to improved patient selection prior to EVT, as well as more aggressive monitoring of high-risk patients. Declarations Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics and Consent to Participate declarations not applicable. Author Contribution All authors fulfill the criteria for authorship, and no one who meets these criteria has been excluded. H.N. performed the data analysis and wrote the manuscript. M.C., Y.T., and G.Z. contributed significantly to the conception of the study, analysis, manuscript preparation. X.Z., Y.Z., and L.Z. assisted in performing the analyses and engaged in constructive discussions. H. M., T. L., X.Y., Z.C., Y.L., S.W., R.C., L.W., Z.W., Y.C., and W. S. carried out the data collection and imaging analysis. All authors read and approved the final manuscript. Acknowledgement We express our gratitude to all the patients who contributed to this study. Data Availability The data supporting the findings of this study can be obtained from the corresponding author upon a reasonable request. References Li XY, Kong XM, Yang CH, Cheng ZF, Lv JJ, Guo H, Liu XH (2024) Global, regional, and national burden of ischemic stroke, 1990–2021: an analysis of data from the global burden of disease study 2021. EClinicalMedicine 75:102758. 10.1016/j.eclinm.2024.102758 Goyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, Dávalos A, Majoie CB, van der Lugt A, de Miquel MA et al (2016) Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 387:1723–1731. 10.1016/s0140-6736(16)00163-x Bendszus M, Fiehler J, Subtil F, Bonekamp S, Aamodt AH, Fuentes B, Gizewski ER, Hill MD, Krajina A, Pierot L et al (2023) Endovascular thrombectomy for acute ischaemic stroke with established large infarct: multicentre, open-label, randomised trial. Lancet 402:1753–1763. 10.1016/s0140-6736(23)02032-9 Costalat V, Jovin TG, Albucher JF, Cognard C, Henon H, Nouri N, Gory B, Richard S, Marnat G, Sibon I et al (2024) Trial of Thrombectomy for Stroke with a Large Infarct of Unrestricted Size. N Engl J Med 390:1677–1689. 10.1056/NEJMoa2314063 Huo X, Ma G, Tong X, Zhang X, Pan Y, Nguyen TN, Yuan G, Han H, Chen W, Wei M et al (2023) Trial of Endovascular Therapy for Acute Ischemic Stroke with Large Infarct. N Engl J Med 388:1272–1283. 10.1056/NEJMoa2213379 Sarraj A, Hassan AE, Abraham MG, Ortega-Gutierrez S, Kasner SE, Hussain MS, Chen M, Blackburn S, Sitton CW, Churilov L et al (2023) Trial of Endovascular Thrombectomy for Large Ischemic Strokes. N Engl J Med 388:1259–1271. 10.1056/NEJMoa2214403 Yoo AJ, Zaidat OO, Sheth SA, Rai AT, Ortega-Gutierrez S, Given CA 2nd, Zaidi SF, Grandhi R, Cuellar H, Mokin M et al (2024) Thrombectomy for Stroke With Large Infarct on Noncontrast CT: The TESLA Randomized Clinical Trial. JAMA 332:1355–1366. 10.1001/jama.2024.13933 Yoshimura S, Sakai N, Yamagami H, Uchida K, Beppu M, Toyoda K, Matsumaru Y, Matsumoto Y, Kimura K, Takeuchi M et al (2022) Endovascular Therapy for Acute Stroke with a Large Ischemic Region. N Engl J Med 386:1303–1313. 10.1056/NEJMoa2118191 Ng FC, Yassi N, Sharma G, Brown SB, Goyal M, Majoie C, Jovin TG, Hill MD, Muir KW, Saver JL et al (2021) Cerebral Edema in Patients With Large Hemispheric Infarct Undergoing Reperfusion Treatment: A HERMES Meta-Analysis. Stroke 52:3450–3458. 10.1161/strokeaha.120.033246 Kimberly WT, Dutra BG, Boers AMM, Alves H, Berkhemer OA, van den Berg L, Sheth KN, Roos Y, van der Lugt A, Beenen LFM et al (2018) Association of Reperfusion With Brain Edema in Patients With Acute Ischemic Stroke: A Secondary Analysis of the MR CLEAN Trial. JAMA Neurol 75:453–461. 10.1001/jamaneurol.2017.5162 Vahedi K, Hofmeijer J, Juettler E, Vicaut E, George B, Algra A, Amelink GJ, Schmiedeck P, Schwab S, Rothwell PM et al (2007) Early decompressive surgery in malignant infarction of the middle cerebral artery: a pooled analysis of three randomised controlled trials. Lancet Neurol 6:215–222. 10.1016/s1474-4422(07)70036-4 Simard JM, Kent TA, Chen M, Tarasov KV, Gerzanich V (2007) Brain oedema in focal ischaemia: molecular pathophysiology and theoretical implications. Lancet Neurol 6:258–268. 10.1016/s1474-4422(07)70055-8 Maramattom BV, Bahn MM, Wijdicks EF (2004) Which patient fares worse after early deterioration due to swelling from hemispheric stroke? Neurology 63:2142–2145. 10.1212/01.wnl.0000145626.30318.8a Wheeler HM, Mlynash M, Inoue M, Tipirnini A, Liggins J, Bammer R, Lansberg MG, Kemp S, Zaharchuk G, Straka M et al (2015) The growth rate of early DWI lesions is highly variable and associated with penumbral salvage and clinical outcomes following endovascular reperfusion. Int J Stroke 10:723–729. 10.1111/ijs.12436 von Kummer R, Broderick JP, Campbell BC, Demchuk A, Goyal M, Hill MD, Treurniet KM, Majoie CB, Marquering HA, Mazya MV et al (2015) The Heidelberg Bleeding Classification: Classification of Bleeding Events After Ischemic Stroke and Reperfusion Therapy. Stroke 46:2981–2986. 10.1161/strokeaha.115.010049 Huttner HB, Schwab S (2009) Malignant middle cerebral artery infarction: clinical characteristics, treatment strategies, and future perspectives. Lancet Neurol 8:949–958. 10.1016/s1474-4422(09)70224-8 Niu Y, Zhang Q, Jiang Z, Li W, Chen Z (2024) Middle meningeal artery embolization for the treatment of unilateral large chronic subdural hematoma patients with significant midline shift: A single-center experience. Interv Neuroradiol 15910199241239706. 10.1177/15910199241239706 Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341. 10.1016/j.mri.2012.05.001 https://pyradiomics.readthedocs.io/en/latest/features.html Ostwaldt AC, Battey TWK, Irvine HJ, Campbell BCV, Davis SM, Donnan GA, Kimberly WT (2018) Comparative Analysis of Markers of Mass Effect after Ischemic Stroke. J Neuroimaging 28:530–534. 10.1111/jon.12525 Pillai DR, Dittmar MS, Baldaranov D, Heidemann RM, Henning EC, Schuierer G, Bogdahn U, Schlachetzki F (2009) Cerebral ischemia-reperfusion injury in rats–a 3 T MRI study on biphasic blood-brain barrier opening and the dynamics of edema formation. J Cereb Blood Flow Metab 29:1846–1855. 10.1038/jcbfm.2009.106 Yang GY, Betz AL (1994) Reperfusion-induced injury to the blood-brain barrier after middle cerebral artery occlusion in rats. Stroke 25:1658–1664 discussion 1664 – 1655. 10.1161/01.str.25.8.1658 Broocks G, Kemmling A, Kniep H, Meyer L, Faizy TD, Hanning U, Rimmele LD, Klapproth S, Schön G, Zeleňák K et al (2023) Edema Reduction versus Penumbra Salvage: Investigating Treatment Effects of Mechanical Thrombectomy in Ischemic Stroke. Ann Neurol. 10.1002/ana.26802 Nie X, Liu J, Yan B, Ng FC, Liu S, Wang Y, Wang M, Zheng L, Wang Z, Wang Y et al (2025) Cerebral Edema Progression and Outcomes in Large Infarct Patients Undergoing Endovascular Thrombectomy. Ann Neurol. 10.1002/ana.27235 Bai J, Lyden PD (2015) Revisiting cerebral postischemic reperfusion injury: new insights in understanding reperfusion failure, hemorrhage, and edema. Int J Stroke 10:143–152. 10.1111/ijs.12434 Guo W, Xu J, Zhao W, Zhang M, Ma J, Chen J, Duan J, Ma Q, Song H, Li S et al (2022) A nomogram for predicting malignant cerebral artery infarction in the modern thrombectomy era. Front Neurol 13:934051. 10.3389/fneur.2022.934051 Additional Declarations No competing interests reported. Supplementary Files FigureS1.pdf Figure S1. (a): Follow-up CT scan within 24 hours after thrombectomy, showing a head tilt. (b): Reformatting of the CT image using 3D Slicer to align the bilateral pupils in the same horizontal plane, indicated by the red arrows. (c): The adjusted image showing the selected maximum slice at the point of maximal deviation from the line drawn between the anterior and posterior attachments of the falx cerebri. FigureS2.pdf Figure S2. Selection of radiomic features using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. (a) LASSO coefficient profiles for the 73 radiomic features, plotted against the log(λ) sequence. (b) The selection process of the tuning parameter (λ) in the LASSO model was performed using 4-fold cross-validation with the minimum criteria. The plot shows the area under the receiver operating characteristic curve as a function of log(λ), with dotted vertical lines indicating the optimal value chosen based on the minimum criteria and the 1 standard error of the minimum criteria. FigureS3.pdf Figure S3. Calibration of the nomogram evaluated in both the training cohort (a) and the validation cohort (b). The reference line, representing the ideal nomogram, is shown as a bold gray line. The solid line represents the correction for any bias in the nomogram, while the dotted line indicates the performance of the nomogram. Cite Share Download PDF Status: Published Journal Publication published 07 Nov, 2025 Read the published version in Neuroradiology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7191348","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492291167,"identity":"521640c4-f25f-4024-a9c4-7b62cec572f9","order_by":0,"name":"Nannan Han","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nannan","middleName":"","lastName":"Han","suffix":""},{"id":492291168,"identity":"209b9c67-17b5-4f08-94d5-be499b8b23f0","order_by":1,"name":"Xiaobo Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Zhang","suffix":""},{"id":492291169,"identity":"0851ad2d-55d2-488c-adfc-63a26f420719","order_by":2,"name":"Yu Zhang","email":"","orcid":"","institution":"Northwest Women's and Children's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":492291170,"identity":"64af7f1e-0de1-4328-917e-7627335b253b","order_by":3,"name":"Leshi Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Leshi","middleName":"","lastName":"Zhang","suffix":""},{"id":492291171,"identity":"987290f5-e189-42fe-8d11-dc22d7fa3727","order_by":4,"name":"Haojun Ma","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haojun","middleName":"","lastName":"Ma","suffix":""},{"id":492291172,"identity":"e7ad54d3-83cf-4f5b-81a2-ad5ec09504c1","order_by":5,"name":"Tengfei Li","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tengfei","middleName":"","lastName":"Li","suffix":""},{"id":492291173,"identity":"9ad86f1e-840f-4d64-9117-e814c3b9276d","order_by":6,"name":"Xudong Yan","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xudong","middleName":"","lastName":"Yan","suffix":""},{"id":492291175,"identity":"4247a4ed-48d6-4385-9e52-98e0334da767","order_by":7,"name":"Zhi Chen","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhi","middleName":"","lastName":"Chen","suffix":""},{"id":492291176,"identity":"bb322f84-26a7-4fbe-ad5e-1b75f7e2af32","order_by":8,"name":"Ying Lu","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Lu","suffix":""},{"id":492291178,"identity":"997e2789-cb71-4639-9a72-f39924e79954","order_by":9,"name":"Song Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Wang","suffix":""},{"id":492291182,"identity":"6562d360-afca-4266-a658-722656cbb5c9","order_by":10,"name":"Ruomeng Chai","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruomeng","middleName":"","lastName":"Chai","suffix":""},{"id":492291184,"identity":"4aabc989-128b-4def-8258-55586965853c","order_by":11,"name":"Li Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":492291189,"identity":"88153e5a-3a7a-4b30-84f4-f9f647ce41da","order_by":12,"name":"Zhen Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Wang","suffix":""},{"id":492291191,"identity":"accc8a70-7e84-46a4-bcda-c76930924897","order_by":13,"name":"Ye Cheng","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Cheng","suffix":""},{"id":492291195,"identity":"fc53a6af-0a50-4628-90d3-6b475613dd67","order_by":14,"name":"Wenzhen Shi","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenzhen","middleName":"","lastName":"Shi","suffix":""},{"id":492291198,"identity":"ef950bb0-894c-465c-a0d9-533fad6c1303","order_by":15,"name":"Gejuan Zhang","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gejuan","middleName":"","lastName":"Zhang","suffix":""},{"id":492291199,"identity":"f646cd14-3e06-44f0-94e7-00f9d7f58558","order_by":16,"name":"Ye Tian","email":"","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Tian","suffix":""},{"id":492291202,"identity":"73a64e61-815f-4ce8-a8e3-a26dd2642576","order_by":17,"name":"Mingze Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACCSTywAcDGx5+/gbitTAenFGQJiM54wBRWsCA+TDPh8M2Bg0J+HXwz25+9vBrjkWevP/hA4d5DM7zGDAcYPzwMQePJXeOmRvLbpMoNjxwLOHgHIPbPObMDcySM7fh1mIgkWAmLblNInFjY4/BgTdALZYNB9iYefFqSf8G0dLM/+EAj8E5HoMDCYS05JhJfgRqmc/Gw3AQqJ6wFokbOWXSjEAtG3jYDA7OMEjmkZxxsBmvX/hnpG+T/LmtLnF+/+HHHz78sbPn528++OEjHi0gwMwDcuEBOJ+xAb96kJIfQEKesLpRMApGwSgYqQAAVs5WLZ0z+pwAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Hospital of Northwest University, Xi’an No.3 Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mingze","middleName":"","lastName":"Chang","suffix":""}],"badges":[],"createdAt":"2025-07-23 02:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7191348/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7191348/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00234-025-03818-4","type":"published","date":"2025-11-07T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88005369,"identity":"091e4bda-1f5c-4b2a-b6a8-495a7e179e4c","added_by":"auto","created_at":"2025-07-31 10:40:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":407723,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the patients included in the study. DWI, diffusion weighted imaging; mRS, modified Rankin Scale; ICC, intraclass correlation coefficient; RFs, radiomic features; CFs, clinical features; LASSO, least absolute shrinkage and selection operator.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/db4fb3cf28ae31c7e84a6ed7.png"},{"id":88005370,"identity":"d470622b-8b6a-4701-a16c-e6995c22577e","added_by":"auto","created_at":"2025-07-31 10:40:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2600698,"visible":true,"origin":"","legend":"\u003cp\u003eDiffusion-weighted imaging (DWI) (a), Fluid-attenuated inversion recovery (FLAIR) (b), and magnetic resonance angiography (MRA) (c) maps of a patient with acute ischemic stroke due to right internal carotid artery occlusion. Complete recanalization following mechanical thrombectomy is shown in panel (d). A follow-up CT scan (e), performed 24 hours post-thrombectomy, reveals mild edema with less than 10 mm of midline shift at the early stage. DWI (f), FLAIR (g), and MRA (h) maps of a second patient with acute ischemic stroke due to left middle cerebral artery occlusion. Complete recanalization after thrombectomy is shown in panel (i). A follow-up CT scan (j) performed 24 hours after thrombectomy demonstrates early edema with more than 10 mm of midline shift.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/fa53d3c007f54f6dd8938cce.png"},{"id":88005371,"identity":"e4f06fdd-6f61-49c0-a727-a42528a43530","added_by":"auto","created_at":"2025-07-31 10:40:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":464755,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting midline shift. Points were assigned for mTICI, NIHSS, gldm_DependenceNonUniformity, and shape_MeshVolume by drawing a line upward from the corresponding values. The total points were calculated by summing the individual scores of the four variables included in the nomogram. For example, a patient with an mTICI grade 3 (18 points), an NIHSS score of 21 (25 points), a gldm_DependenceNonUniformity value of 3159.74 (50 points), and a shape_MeshVolume of 188.92 mm3 (45 points) was assessed. The patient’s total points score was 138, corresponding to a diagnostic probability of more than 80%, indicating a midline shift greater than 10 mm within 24 hours after thrombectomy. mTICI indicates the modified thrombolysis in cerebral infarction score. NIHSS indicates the National Institutes of Health Stroke Scale.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/1a34b6d037e7a1a21e49ec47.png"},{"id":88003502,"identity":"06a815dd-c7cd-466f-878c-d13f2fa62eb4","added_by":"auto","created_at":"2025-07-31 10:32:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":353920,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of the nomogram for predicting early severe midline shift within 24 hours after endovascular thrombectomy in the training dataset (A) and the validation dataset (B). AUC represents the area under the curve.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/99aba98d8af64e80a3156f5f.png"},{"id":95564080,"identity":"8cff57e4-6d81-4a6b-a86a-392093bea84a","added_by":"auto","created_at":"2025-11-10 16:07:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4987885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/7205998e-26ff-4cc0-a8cc-82ae7fe15967.pdf"},{"id":88003496,"identity":"03ce3a91-4f9c-483a-8a2f-1619f7dcdd2a","added_by":"auto","created_at":"2025-07-31 10:32:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1051458,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. (a): Follow-up CT scan within 24 hours after thrombectomy, showing a head tilt. (b): Reformatting of the CT image using 3D Slicer to align the bilateral pupils in the same horizontal plane, indicated by the red arrows. (c): The adjusted image showing the selected maximum slice at the point of maximal deviation from the line drawn between the anterior and posterior attachments of the falx cerebri.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/80463eb4a3a976dd0beca9c4.pdf"},{"id":88003497,"identity":"bffb9b37-460c-4ecf-a85c-5d77a2da4d54","added_by":"auto","created_at":"2025-07-31 10:32:39","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":631129,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2. Selection of radiomic features using the Least Absolute Shrinkage and Selection Operator (LASSO) binary logistic regression model. (a) LASSO coefficient profiles for the 73 radiomic features, plotted against the log(λ) sequence. (b) The selection process of the tuning parameter (λ) in the LASSO model was performed using 4-fold cross-validation with the minimum criteria. The plot shows the area under the receiver operating characteristic curve as a function of log(λ), with dotted vertical lines indicating the optimal value chosen based on the minimum criteria and the 1 standard error of the minimum criteria.\u003c/p\u003e","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/6e2b7817b9ed813f66a0aeb8.pdf"},{"id":88003504,"identity":"dab76b0a-67b6-4b61-bcc4-a17cd3be465d","added_by":"auto","created_at":"2025-07-31 10:32:39","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":837208,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3. Calibration of the nomogram evaluated in both the training cohort (a) and the validation cohort (b). The reference line, representing the ideal nomogram, is shown as a bold gray line. The solid line represents the correction for any bias in the nomogram, while the dotted line indicates the performance of the nomogram.\u003c/p\u003e","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7191348/v1/ec31dcc7ba2e8b8fad0dfd88.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Early Severe Midline Shift in Acute Ischemic Stroke Following Endovascular Thrombectomy Within 24 Hours","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke remains a significant cause of morbidity and mortality worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Over the past decade, endovascular thrombectomy (EVT) has emerged as a critical therapeutic method, demonstrating significant efficacy in improving clinical outcomes through arterial recanalization.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Expedited EVT implementation is particularly vital for patients with large ischemic cores, as timely recanalization may lead to a remarkable functional outcome.\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHowever, despite the reperfusion achieved by EVT, midline shift (MLS) remains a formidable challenge in the post-EVT period.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e MLS, often indicative of substantial cerebral edema and increased intracranial pressure, significantly increases the risk of poor outcomes.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e The pathophysiology of MLS involves ischemia-induced capillary dysfunction, which initiates a cascade of ionic imbalance and inflammatory damage. These processes collectively drive cerebral edema, tissue swelling, and functional impairment.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eBoth the severity and early occurrence of midline shift (MLS) are associated with poor prognosis. In clinical practice, patients with early and severe (ES) MLS, simultaneously detected by follow-up computed tomography (CT) scans within 24 hours after EVT, often experience poor outcomes. Nevertheless, reliable neurological monitoring for ES-MLS remains clinically challenging due to postoperative sedation requirements and severe baseline neurological deficits before EVT. Early prediction of ES-MLS has become a critical priority in guiding time-sensitive therapeutic interventions. Because of delayed intervention in patients showing signs of cerebral herniation before decompressive hemicraniectomy increased risk of unfavorable outcomes.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo our knowledge, no studies currently systematically describe and predict ES-MLS. Since the positive results of the RCT with a large ischemic core, the opportunity for these patients to undergo EVT has been increasing. Most of the large ischemic core studies have included diffusion-weighted imaging (DWI) in the preoperative evaluation method, including the published TENSION\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, ANGEL-ASPECT\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, SELECT2\u003csup\u003e6\u003c/sup\u003e, LASTE\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Among these, the RESCUE-Japan LIMIT study\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e reported that 88 out of 101 patients in the EVT group and 87 out of 102 patients in the non-EVT group were evaluated by DWI. By integrating quantitative radiomic features extracted from pre-EVT DWI with clinical features, we aim to describe ES-MLS and construct an intuitive risk stratification tool that enables clinicians to identify high-risk individuals.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003cp\u003eFor the analysis of RFs, quality control was performed using the intraclass correlation coefficient (ICC), with RFs exhibiting ICC values lower than 0.75 being excluded. The remaining RFs were selected using the least absolute shrinkage and selection operator (LASSO) in the training cohort. A nomogram was constructed based on the selected CFs and RFs, and its discriminative ability was evaluated using a separate validation dataset. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the nomogram's performance, while calibration plots were employed to examine the agreement between actual and nomogram-predicted ES-MLS values. The ICC analysis was conducted using Python 3.9.7 and the Pinguin 0.5.3 package, ROC analysis was performed using MedCalc 19.2.0, and RF extraction utilized Pyradiomics 3.1.0, Scikit-learn 0.24.2, Pandas 2.1.4, and NumPy 1.26.2 packages. Additional statistical analyses were carried out using SPSS (version 25.0, IBM, NY, USA) and R statistical software (version 4.3.0, R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\u003c/div\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003ePatients Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe patients in this study were recruited from an ongoing prospective cohort study (xxxxxx). We conducted a retrospective analysis of this ongoing study, enrolling cases from July 2018 to July 2024. The study participants were selected based on the following inclusion criteria: (1) thrombectomy procedures performed according to established standards of care, (2) pre-thrombectomy DWI was performed, (3) onset-to-DWI time within 24 hours, (4) presence of large vessel occlusion in the anterior circulation (including the internal carotid artery and M1-M2 segments of the middle cerebral artery) confirmed by DSA, and (5) availability of 90-day mRS follow-up (0–6, with higher scores indicating worse prognosis). The exclusion criteria were: (1) prior history of stroke with an mRS score of 3 to 5, (2) CT or MRI before thrombectomy showing brain tissue swelling with midline shift, (3) known allergies to contrast agents (more severe than skin rash), (4) evidence of acute intracranial hemorrhage on CT or MRI before thrombectomy, (5) high risk of hemorrhage (platelet count \u0026lt; 40,000/uL), (6) DWI motion artifacts that hinder accurate identification of the infarcted area, and (7) simultaneous occlusion in both anterior and posterior circulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e This study was approved by the Institutional Review Board of the Affiliated Hospital of Northwest University (xxxxxx), and written informed consent was obtained from the legally authorized representatives for EVT.\u003c/p\u003e\u003cp\u003e\u003cb\u003eClinical data collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe demographic and clinical characteristics of the patients were analyzed, including age, gender, medical history, and ischemic stroke severity, assessed using the NIHSS score in the emergency department. Other recorded parameters included the side and site of the occlusion, time from stroke onset to DWI, time from DWI to reperfusion, presence of wake-up stroke (calculated as the midpoint between the last normal time and the time symptoms were detected),\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and administration of intravenous recombinant tissue plasminogen activator (rt-PA) or recombinant human TNK tissue-type plasminogen activator (TNK).\u003c/p\u003e\u003cp\u003eFor comparison between the training and validation sets, additional parameters were recorded, including infarction volume, time from DSA to follow-up CT, stroke etiology, parenchymal hemorrhage type 2,\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and reperfusion status according to the modified thrombolysis in cerebral infarction (mTICI) grading system. A functional outcome was defined as a modified Rankin Scale (mRS) score of 0 to 2, a fair outcome as an mRS of 0 to 3, and death (mRS score of 6) was assessed at 90 ± 7 days after thrombectomy via telephone or face-to-face interview with the patient or their relatives, conducted by neurologists.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy outcomes\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary outcome, ES-MLS, was defined based on a combination of clinical and neuroimaging criteria\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e : (1) Neuroimaging evidence of a midline shift ≥ 10 mm on follow-up CT within 24 hours after EVT; and (2) Clinical signs of unilateral to bilateral pupil dilation within 24 hours after thrombectomy when CT was not performed in 6 cases. MLS ≥ 10 mm was chosen because it is the imaging critical value and surgical treatment is advocated for this condition.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA diagnosis of ES-MLS requires the presence of at least one neuroimaging criterion and one clinical criterion. Examples of the ES-MLS flow are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eImage Acquisition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDWI scans were acquired on a PHILIPS Ingenia 3.0T MRI scanner using the following parameters: repetition time (TR) of 2506 ms, echo time (TE) of 68 ms, field of view (FOV) of 230 mm × 230 mm, image matrix size of 152 × 102 pixels, b-values of 0 and 1000 s/mm², slice thickness of 5.0 mm, and an interslice gap of 1.0 mm. The DWI data were automatically uploaded to the Picture Archiving and Communication System (PACS), subsequently downloaded in DICOM format, and sent for evaluation by two independent readers in the laboratory. Two neurologists manually segmented the regions with hyperintense signals in the DWI using 3D Slicer software\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e (version 4.11.2 to 5.2.2). The neurologists were blinded to the thrombectomy procedure results and clinical outcomes.\u003c/p\u003e\u003cp\u003eRadiomic features (RFs)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e were automatically extracted from the segmented hyperintense signal regions using Pyradiomics (version 3.1.0). A total of 107 features were derived from the DWI data, including 18 first-order statistics, 14 shape-based features, 25 features from the Gray Level Co-occurrence Matrix (GLCM), 13 from the Gray Level Dependence Matrix (GLDM), 16 from the Gray Level Run Length Matrix (GLRLM), 16 from the Gray Level Size Zone Matrix (GLSZM), and 5 from the Neighboring Gray Tone Difference Matrix (NGTDM).\u003c/p\u003e\u003cp\u003eFollow-up CT scans were performed 24 hours after EVT unless the patients presented with clinical symptoms such as anisocoria. The CT scans were acquired using a SIEMENS 128-slice scanner with the following parameters: 120 kV, automatic mAs, field of view (FOV) of 200 mm, matrix size of 512 × 512, and slice thickness of 5 mm, or a GE 64-slice scanner with similar parameters: 120 kV, automatic mAs, FOV of 250 mm, matrix size of 512×512, and slice thickness of 5 mm. The images were saved in DICOM format and sent to the imaging laboratory for further analysis. For the follow-up CT images, reformatting was performed using 3D Slicer to align the bilateral pupils in the same horizontal plane. The maximum slice was selected at the point of maximal deviation from the line drawn between the anterior and posterior attachments of the falx cerebri (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eTo analyze the clinical features (CFs), the normality of the data distributions was assessed using histograms and the Shapiro-Wilk test. A p-value larger than 0.05 was considered indicative of a normal distribution. Quantitative variables are expressed as medians with interquartile range (IQR). Bivariate comparisons between CFs were conducted using the χ² test for categorical variables and the Mann-Whitney U test for quantitative variables. Missing data were rare (\u0026lt; 5%) and was not imputed. In the univariate logistic regression analysis, variables with p-values less than 0.1 were further evaluated in a multivariable logistic regression analysis using a backward selection approach. Clinical features with p-values less than 0.05 in the multivariable analysis were deemed statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFrom July 2018 to July 2024, a total of 481 patients were enrolled, with 49 (10.2%) experiencing ES-MLS within 24 hours after thrombectomy. A total of 744 patients undergoing thrombectomy were screened for inclusion; however, 138 (18.5%) were excluded due to posterior circulation artery occlusion (including 2 cases of simultaneous anterior and posterior circulation occlusion), 68 (9.1%) were excluded due to the absence of DWI, and 43 (5.8%) were excluded because the time of symptoms onset to DWI acquisition was more than 24 hours. Additionally, 8 patients (1.1%) with poor-quality DWI images, 3 patients (0.4%) with anterior cerebral artery occlusion, and 3 patients (0.4%) with a history of mRS scores more than 2 were excluded.\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the median age of the included patients was 68 years (IQR 58.0\u0026ndash;76.0), and 191 (39.7%) of the patients were female. The median baseline NIHSS score was 13 (IQR 9\u0026ndash;17). At the 90-day follow-up, 52.8% of patients had an mRS score of 0 to 2, and 17.9% had died. Patients with ES-MLS exhibited a higher proportion of atrial fibrillation, internal carotid artery occlusion, a higher baseline NIHSS score, larger infarction volume, and higher proportion of all type hemorrhage and Parenchymal hemorrhage type 2. Patients with ES-MLS also had poorer outcomes, including a lower proportion of mRS 0 to 2 (0 [0%] vs. 254 [58.8%], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), mRS 0 to 3 (1 [2.0%] vs. 298 [69.0%], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 85.7% had died.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics and characteristics of the Non-ES-MLS and ES-MLS groups.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll\u0026nbsp;patients\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;481)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-ES-MLS\u003c/p\u003e\u003cp\u003egroup\u003c/p\u003e\u003cp\u003e(\u0026nbsp;N\u0026thinsp;=\u0026thinsp;432\u0026nbsp;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eES-MLS\u003c/p\u003e\u003cp\u003egroup\u003c/p\u003e\u003cp\u003e(\u0026nbsp;N\u0026thinsp;=\u0026thinsp;49\u0026nbsp;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026nbsp;Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS\u0026nbsp;0\u0026ndash;2,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e254\u0026nbsp;(52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e254\u0026nbsp;(58.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u0026nbsp;(0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS\u0026nbsp;0\u0026ndash;3,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e299\u0026nbsp;(62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e298\u0026nbsp;(69.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u0026nbsp;(2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS\u0026nbsp;6,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e86\u0026nbsp;(17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u0026nbsp;(10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u0026nbsp;(85.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge,\u0026nbsp;y,\u0026nbsp;median\u0026nbsp;(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68\u0026nbsp;(58.0\u0026ndash;76.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u0026nbsp;(58.0\u0026ndash;76.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70\u0026nbsp;(55.0\u0026ndash;78.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u0026nbsp;sex,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e191\u0026nbsp;(39.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e167\u0026nbsp;(38.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u0026nbsp;(49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious\u0026nbsp;stroke,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93\u0026nbsp;(19.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u0026nbsp;(18.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12\u0026nbsp;(24.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e280\u0026nbsp;(58.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e246\u0026nbsp;(56.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u0026nbsp;(69.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u0026nbsp;mellitus,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88\u0026nbsp;(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u0026nbsp;(18.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u0026nbsp;(18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108\u0026nbsp;(22.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99\u0026nbsp;(22.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9\u0026nbsp;(18.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.470\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial\u0026nbsp;fibrillation,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e165\u0026nbsp;(34.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141\u0026nbsp;(32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24\u0026nbsp;(49.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e169\u0026nbsp;(35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e152\u0026nbsp;(35.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17\u0026nbsp;(34.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIHSS\u0026nbsp;score,\u0026nbsp;median\u0026nbsp;(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u0026nbsp;(9.0\u0026ndash;17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.0\u0026nbsp;(8.0\u0026ndash;17.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.0\u0026nbsp;(13.5\u0026ndash;21.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOcclusion\u0026nbsp;site,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal\u0026nbsp;carotid\u0026nbsp;artery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e216\u0026nbsp;(44.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e187\u0026nbsp;(43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29(59.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u0026nbsp;segment\u0026nbsp;of\u0026nbsp;MCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e189\u0026nbsp;(39.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e174\u0026nbsp;(40.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15\u0026nbsp;(30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u0026nbsp;segment\u0026nbsp;of\u0026nbsp;MCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76\u0026nbsp;(15.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u0026nbsp;(16.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u0026nbsp;(10.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSide,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e254\u0026nbsp;(52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e232\u0026nbsp;(53.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22\u0026nbsp;(44.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfarction\u0026nbsp;volume,\u0026nbsp;mm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e\u0026nbsp;,\u0026nbsp;median\u0026nbsp;(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14.5\u0026nbsp;(5.7\u0026ndash;54.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.8\u0026nbsp;(5.3\u0026ndash;43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119.7\u0026nbsp;(28.4-185.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime\u0026nbsp;from\u0026nbsp;onset\u0026nbsp;to\u0026nbsp;DWI,\u0026nbsp;min,\u0026nbsp;median\u0026nbsp;(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e295.5\u0026nbsp;(161.0-508.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e296.0\u0026nbsp;(160.3-510.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e283.5\u0026nbsp;(162.8-483.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.374\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime\u0026nbsp;from\u0026nbsp;DWI\u0026nbsp;to\u0026nbsp;reperfusion,\u0026nbsp;min,\u0026nbsp;median(IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e148.0\u0026nbsp;(119.0-188.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148.0\u0026nbsp;(119.0-186.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e158.0\u0026nbsp;(102.0-203.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime\u0026nbsp;from\u0026nbsp;DSA\u0026nbsp;to\u0026nbsp;follow-up\u0026nbsp;CT,\u0026nbsp;hour,\u0026nbsp;median\u0026nbsp;(IQR)\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.1\u0026nbsp;(17.6\u0026ndash;24.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.1(17.4\u0026ndash;25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.3\u0026nbsp;(18.1\u0026ndash;24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWake-up\u0026nbsp;stroke,\u0026nbsp;n.\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e119\u0026nbsp;(24.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111\u0026nbsp;(25.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u0026nbsp;(16.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntravenous\u0026nbsp;rt-PA\u0026nbsp;or\u0026nbsp;TNK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e163\u0026nbsp;(33.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e142\u0026nbsp;(32.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21\u0026nbsp;(42.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCause\u0026nbsp;of\u0026nbsp;stroke,\u0026nbsp;n(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmbolic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e256\u0026nbsp;(53.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e224\u0026nbsp;(51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32\u0026nbsp;(65.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtherosclerotic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e200\u0026nbsp;(41.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e186\u0026nbsp;(43.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u0026nbsp;(28.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u0026nbsp;(5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u0026nbsp;(5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u0026nbsp;(6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemorrhage\u0026nbsp;all\u0026nbsp;type\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147\u0026nbsp;(30.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e110\u0026nbsp;(25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37\u0026nbsp;(82.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParenchymal\u0026nbsp;hemorrhage\u0026nbsp;type\u0026nbsp;2\u003csup\u003e\u0026amp;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u0026nbsp;(6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13\u0026nbsp;(42.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emTICI 2b-c,\u0026nbsp;n.(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e429\u0026nbsp;(89.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e389\u0026nbsp;(90.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40\u0026nbsp;(81.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eES-MLS, early severe midline shift; mRS, modified Rankin Scale; IQR, interquartile range; NIHSS, national institute of the health stroke scale; MCA, middle cerebral artery; DWI, diffusion weighted imaging; DSA, digital subtraction angiography; CT, computed tomography; rt-PA, intravenous recombinant tissue plasminogen activator;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eTNK, recombinant human TNK tissue-type plasminogen activator; mTICI, modified treatment in cerebral ischemia; \u003csup\u003e\u0026amp;\u003c/sup\u003eSix patients missed a CT scan after thrombectomy within 24 hours, and the analysis was conducted using the actual values.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter randomization, the patients in the training and validation sets were well-matched, with the exception of wake-up stroke (81 [22.4%] vs. 38 [31.7%], p\u0026thinsp;=\u0026thinsp;0.042) and reperfusion status (316 [87.5%] vs. 113 [94.2%], p\u0026thinsp;=\u0026thinsp;0.043). No significant differences were noted in medical history, ischemic stroke severity, or other recorded parameters (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results of the logistic regression analysis for ES-MLS in the training dataset are in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The univariate analysis indicated that atrial fibrillation, baseline NIHSS score, and mTICI status were potential predictors of ES-MLS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). The multivariate analysis revealed that NIHSS score (odds ratio [OR], 1.103; 95% confidence interval [CI], 1.042\u0026ndash;1.168; P\u0026thinsp;=\u0026thinsp;0.001), and mTICI (OR, 0.336; 95% CI, 0.136\u0026ndash;0.828; P\u0026thinsp;=\u0026thinsp;0.018) were significant predictors of ES-MLS. Additionally, 107 RFs were extracted from the segmented regions of two readers, and 73 RFs with an ICC greater than 0.75 were selected for further analysis. Subsequently, LASSO analysis was performed, and two RFs, gldm_DependenceNonUniformity and shape_MeshVolume, were selected as significant predictors of the ES-MLS (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression analysis of the risk factors associated with ES-MLS in the training cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnadjusted\u0026nbsp;OR (95%\u0026nbsp;CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep\u0026nbsp;Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eadjusted\u0026nbsp;OR (95%\u0026nbsp;CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u0026nbsp;Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge,\u0026nbsp;y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.003(0.975\u0026ndash;1.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.681(0.835\u0026ndash;3.382)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious\u0026nbsp;stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.213(0.526\u0026ndash;2.796)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.151(0.560\u0026ndash;2.367)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u0026nbsp;mellitus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.042(0.413\u0026ndash;2.631)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHyperlipidemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.558(0.209\u0026ndash;1.488)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial\u0026nbsp;fibrillation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.045(1.013\u0026ndash;4.129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.652(0.785\u0026ndash;3.478)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.830(0.392\u0026ndash;1.754)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIHSS\u0026nbsp;score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.105(1.046\u0026ndash;1.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.103(1.042\u0026ndash;1.168)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOcclusion\u0026nbsp;site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternal\u0026nbsp;carotid\u0026nbsp;artery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.680(0.834\u0026ndash;3.385)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u0026nbsp;segment\u0026nbsp;of\u0026nbsp;MCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.748(0.360\u0026ndash;1.554)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2\u0026nbsp;segment\u0026nbsp;of\u0026nbsp;MCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.607(0.206\u0026ndash;1.786)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSide\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.631(0.312\u0026ndash;1.277)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime\u0026nbsp;from\u0026nbsp;onset\u0026nbsp;to\u0026nbsp;DWI,\u0026nbsp;min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.999(0.998\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime\u0026nbsp;from\u0026nbsp;DWI\u0026nbsp;to\u0026nbsp;reperfusion,\u0026nbsp;min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.999(0.995\u0026ndash;1.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emTICI 2b-c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.432(0.183\u0026ndash;1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.336(0.136\u0026ndash;0.828)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmbolic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.759(0.847\u0026ndash;3.653)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWake-up\u0026nbsp;stroke\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.548(0.206\u0026ndash;1.462)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.230\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntravenous\u0026nbsp;rt-PA/TNK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.983(0.472\u0026ndash;2.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eES-MLS, early severe midline shift; OR, odd ratio; CI, confidence interval; NIHSS, ational institute of the health stroke scale; MCA, middle cerebral artery; DWI: diffusion weighted imaging; mTICI, modified treatment in cerebral ischemia; rt-PA, intravenous recombinant tissue plasminogen activator; TNK, recombinant human TNK tissue-type plasminogen activator.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results indicated that higher total points, as calculated by summing the assigned number of points for each predictor in the nomogram, corresponded to an increased risk of unfavorable outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The performance of the nomogram was assessed by the AUC of ROC, which was found to be 0.844 (95% CI, 0.803\u0026ndash;0.880, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the training dataset and 0.823 (95% CI, 0.743\u0026ndash;0.887, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the validation dataset, indicating good predictive power (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The calibration plot was used to compare the prediction of ES-MLS made by the nomogram with actual observations (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we aimed to describe an uncommon yet potentially fatal complication, ES-MLS, and to develop an imaging-clinical predictive model for its early detection following EVT. Our single-center study found a 10.2% incidence of ES-MLS in this cohort. Remarkably, 85.7% of patients with ES-MLS ≥ 10 mm after EVT had died within 90-day, and all affected individuals experienced poor clinical outcomes. The radiomic-clinical nomogram developed, which incorporates the NIHSS score, recanalization status, and two radiomic features exhibited good predictive performance, with an AUC of 0.844 in the training dataset and 0.823 in the validation dataset. This predictive tool provides clinicians with an intuitive instrument for early risk stratification and therapeutic decision-making.\u003c/p\u003e\u003cp\u003eAnimal studies consistently show that ischemia-reperfusion injury promotes cerebral edema.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e However, the effect of EVT on the progression of cerebral edema remains controversial. Broocks et al.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e suggested that EVT improves outcomes by reducing cerebral edema, with an estimated mediation proportion of 66%. In contrast, a secondary analysis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e of the ANGEL-ASPECT trial revealed that EVT increased MLS in patients with a large ischemic core, with MLS \u0026gt; 5mm occurring in 26.9% of the EVT group compared to 15.2% in the non-EVT group (adjusted odds ratio: 2.61) at 24-hour follow-up. This phenomenon partially mediated the poor outcomes after EVT (mediation proportion: -25%; 95% CI: -46.54 to -4.10).\u003c/p\u003e\u003cp\u003eThe controversy can be explained from three perspectives: 1. The size of the infarct core affects MLS: For patients with small infarct cores, although EVT-induced reperfusion may lead to increased edema in the infarcted tissue, but edema is insufficient to cause MLS. In contrast, patients with small infarcts who do not undergo EVT, the lack of reperfusion causes further enlargement of the infarct core over time, leading to MLS. 2. Large infarcts with DWI high signal and reperfusion promote MLS: DWI imaging showing extensive high signals in large infarcts indicates limited salvageable brain tissue. Reperfusion following EVT induces transient post-EVT edema.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e This reperfusion injury exacerbates the disruption of the blood-brain barrier, increasing vascular permeability and leading to vasogenic edema.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Studies have shown that patients with infarct volumes greater than 130 ml may experience MLS following EVT, suggesting that reperfusion treatment may have a detrimental effect in these cases\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. 3. Large infarcts with DWI low signal and reperfusion affect MLS: In early-stage ischemic stroke patients, DWI imaging reveals a low signal and short-term reperfusion can restore blood flow, preventing the transition from low to high signal intensity. Furthermore, the administration of anti-edema drugs following reperfusion may more effectively target the edematous regions, reducing edema and slowing the progression of MLS. In contrast, patients without reperfusion may experience significant tissue damage, leading to persistent edema due to cellular death.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eThe current investigation identified NIHSS score and recanalization status (mTICI) as clinical determinants of ES-MLS, consistent with malignant middle cerebral artery infarction within 5 days after EVT.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e The radiomic parameter shape_MeshVolume emerged as the most significant imaging predictor derived from pre-EVT DWI sequences. The volume is calculated from the triangle mesh of the region of interest, which quantifies ischemic lesion topology through three-dimensional surface mesh decomposition\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and is highly correlated with infarct volume. The second critical radiomic feature, gldm_DependenceNonUniformity (DN), indicates that a higher DN value reflects significant variation in pixel dependencies, resulting in an uneven distribution of tissue density and gray levels. The greater the signal difference between high-signal DWI lesions and normal brain tissue, the more severe the brain injury.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis investigation has several limitations. First, the single-center retrospective design introduces inherent selection biases and limits generalizability. Second, our predictive model specifically focused on preoperative variables at the time of EVT procedure finish, excluding postoperative monitoring variables such as Glasgow Coma Scale scores and changes in NIHSS after EVT. Third, the radiomic analysis chooses first-order features to consider the clinical interpretability. This approach mitigated overfitting risks associated with higher-order texture parameters. Fourth, manual infarct segmentation remains labor-intensive, which poses challenges for real-time clinical implementation. Emerging artificial intelligence-driven segmentation algorithms could enhance both efficiency and reproducibility in future applications. Finally, perfusion images from MR imaging were not considered in this study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThese findings emphasize the importance of early detection of ES-MLS and may contribute to improved patient selection prior to EVT, as well as more aggressive monitoring of high-risk patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics and Consent to Participate declarations\u003c/h2\u003e\u003cp\u003enot applicable.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors fulfill the criteria for authorship, and no one who meets these criteria has been excluded. H.N. performed the data analysis and wrote the manuscript. M.C., Y.T., and G.Z. contributed significantly to the conception of the study, analysis, manuscript preparation. X.Z., Y.Z., and L.Z. assisted in performing the analyses and engaged in constructive discussions. H. M., T. L., X.Y., Z.C., Y.L., S.W., R.C., L.W., Z.W., Y.C., and W. S. carried out the data collection and imaging analysis. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe express our gratitude to all the patients who contributed to this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study can be obtained from the corresponding author upon a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLi XY, Kong XM, Yang CH, Cheng ZF, Lv JJ, Guo H, Liu XH (2024) Global, regional, and national burden of ischemic stroke, 1990\u0026ndash;2021: an analysis of data from the global burden of disease study 2021. EClinicalMedicine 75:102758. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eclinm.2024.102758\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2024.102758\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, D\u0026aacute;valos A, Majoie CB, van der Lugt A, de Miquel MA et al (2016) Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 387:1723\u0026ndash;1731. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(16)00163-x\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(16)00163-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBendszus M, Fiehler J, Subtil F, Bonekamp S, Aamodt AH, Fuentes B, Gizewski ER, Hill MD, Krajina A, Pierot L et al (2023) Endovascular thrombectomy for acute ischaemic stroke with established large infarct: multicentre, open-label, randomised trial. Lancet 402:1753\u0026ndash;1763. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(23)02032-9\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(23)02032-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCostalat V, Jovin TG, Albucher JF, Cognard C, Henon H, Nouri N, Gory B, Richard S, Marnat G, Sibon I et al (2024) Trial of Thrombectomy for Stroke with a Large Infarct of Unrestricted Size. N Engl J Med 390:1677\u0026ndash;1689. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2314063\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2314063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuo X, Ma G, Tong X, Zhang X, Pan Y, Nguyen TN, Yuan G, Han H, Chen W, Wei M et al (2023) Trial of Endovascular Therapy for Acute Ischemic Stroke with Large Infarct. N Engl J Med 388:1272\u0026ndash;1283. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2213379\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2213379\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSarraj A, Hassan AE, Abraham MG, Ortega-Gutierrez S, Kasner SE, Hussain MS, Chen M, Blackburn S, Sitton CW, Churilov L et al (2023) Trial of Endovascular Thrombectomy for Large Ischemic Strokes. N Engl J Med 388:1259\u0026ndash;1271. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2214403\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2214403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoo AJ, Zaidat OO, Sheth SA, Rai AT, Ortega-Gutierrez S, Given CA 2nd, Zaidi SF, Grandhi R, Cuellar H, Mokin M et al (2024) Thrombectomy for Stroke With Large Infarct on Noncontrast CT: The TESLA Randomized Clinical Trial. JAMA 332:1355\u0026ndash;1366. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2024.13933\u003c/span\u003e\u003cspan address=\"10.1001/jama.2024.13933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoshimura S, Sakai N, Yamagami H, Uchida K, Beppu M, Toyoda K, Matsumaru Y, Matsumoto Y, Kimura K, Takeuchi M et al (2022) Endovascular Therapy for Acute Stroke with a Large Ischemic Region. N Engl J Med 386:1303\u0026ndash;1313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa2118191\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa2118191\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNg FC, Yassi N, Sharma G, Brown SB, Goyal M, Majoie C, Jovin TG, Hill MD, Muir KW, Saver JL et al (2021) Cerebral Edema in Patients With Large Hemispheric Infarct Undergoing Reperfusion Treatment: A HERMES Meta-Analysis. Stroke 52:3450\u0026ndash;3458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/strokeaha.120.033246\u003c/span\u003e\u003cspan address=\"10.1161/strokeaha.120.033246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKimberly WT, Dutra BG, Boers AMM, Alves H, Berkhemer OA, van den Berg L, Sheth KN, Roos Y, van der Lugt A, Beenen LFM et al (2018) Association of Reperfusion With Brain Edema in Patients With Acute Ischemic Stroke: A Secondary Analysis of the MR CLEAN Trial. JAMA Neurol 75:453\u0026ndash;461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamaneurol.2017.5162\u003c/span\u003e\u003cspan address=\"10.1001/jamaneurol.2017.5162\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVahedi K, Hofmeijer J, Juettler E, Vicaut E, George B, Algra A, Amelink GJ, Schmiedeck P, Schwab S, Rothwell PM et al (2007) Early decompressive surgery in malignant infarction of the middle cerebral artery: a pooled analysis of three randomised controlled trials. Lancet Neurol 6:215\u0026ndash;222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1474-4422(07)70036-4\u003c/span\u003e\u003cspan address=\"10.1016/s1474-4422(07)70036-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSimard JM, Kent TA, Chen M, Tarasov KV, Gerzanich V (2007) Brain oedema in focal ischaemia: molecular pathophysiology and theoretical implications. Lancet Neurol 6:258\u0026ndash;268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1474-4422(07)70055-8\u003c/span\u003e\u003cspan address=\"10.1016/s1474-4422(07)70055-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaramattom BV, Bahn MM, Wijdicks EF (2004) Which patient fares worse after early deterioration due to swelling from hemispheric stroke? Neurology 63:2142\u0026ndash;2145. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1212/01.wnl.0000145626.30318.8a\u003c/span\u003e\u003cspan address=\"10.1212/01.wnl.0000145626.30318.8a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWheeler HM, Mlynash M, Inoue M, Tipirnini A, Liggins J, Bammer R, Lansberg MG, Kemp S, Zaharchuk G, Straka M et al (2015) The growth rate of early DWI lesions is highly variable and associated with penumbral salvage and clinical outcomes following endovascular reperfusion. Int J Stroke 10:723\u0026ndash;729. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ijs.12436\u003c/span\u003e\u003cspan address=\"10.1111/ijs.12436\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003evon Kummer R, Broderick JP, Campbell BC, Demchuk A, Goyal M, Hill MD, Treurniet KM, Majoie CB, Marquering HA, Mazya MV et al (2015) The Heidelberg Bleeding Classification: Classification of Bleeding Events After Ischemic Stroke and Reperfusion Therapy. Stroke 46:2981\u0026ndash;2986. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/strokeaha.115.010049\u003c/span\u003e\u003cspan address=\"10.1161/strokeaha.115.010049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuttner HB, Schwab S (2009) Malignant middle cerebral artery infarction: clinical characteristics, treatment strategies, and future perspectives. Lancet Neurol 8:949\u0026ndash;958. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1474-4422(09)70224-8\u003c/span\u003e\u003cspan address=\"10.1016/s1474-4422(09)70224-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiu Y, Zhang Q, Jiang Z, Li W, Chen Z (2024) Middle meningeal artery embolization for the treatment of unilateral large chronic subdural hematoma patients with significant midline shift: A single-center experience. Interv Neuroradiol 15910199241239706. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/15910199241239706\u003c/span\u003e\u003cspan address=\"10.1177/15910199241239706\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M et al (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323\u0026ndash;1341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mri.2012.05.001\u003c/span\u003e\u003cspan address=\"10.1016/j.mri.2012.05.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pyradiomics.readthedocs.io/en/latest/features.html\u003c/span\u003e\u003cspan address=\"https://pyradiomics.readthedocs.io/en/latest/features.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOstwaldt AC, Battey TWK, Irvine HJ, Campbell BCV, Davis SM, Donnan GA, Kimberly WT (2018) Comparative Analysis of Markers of Mass Effect after Ischemic Stroke. J Neuroimaging 28:530\u0026ndash;534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jon.12525\u003c/span\u003e\u003cspan address=\"10.1111/jon.12525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePillai DR, Dittmar MS, Baldaranov D, Heidemann RM, Henning EC, Schuierer G, Bogdahn U, Schlachetzki F (2009) Cerebral ischemia-reperfusion injury in rats\u0026ndash;a 3 T MRI study on biphasic blood-brain barrier opening and the dynamics of edema formation. J Cereb Blood Flow Metab 29:1846\u0026ndash;1855. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/jcbfm.2009.106\u003c/span\u003e\u003cspan address=\"10.1038/jcbfm.2009.106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang GY, Betz AL (1994) Reperfusion-induced injury to the blood-brain barrier after middle cerebral artery occlusion in rats. Stroke 25:1658\u0026ndash;1664 discussion 1664\u0026thinsp;\u0026ndash;\u0026thinsp;1655. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.str.25.8.1658\u003c/span\u003e\u003cspan address=\"10.1161/01.str.25.8.1658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBroocks G, Kemmling A, Kniep H, Meyer L, Faizy TD, Hanning U, Rimmele LD, Klapproth S, Sch\u0026ouml;n G, Zeleň\u0026aacute;k K et al (2023) Edema Reduction versus Penumbra Salvage: Investigating Treatment Effects of Mechanical Thrombectomy in Ischemic Stroke. Ann Neurol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ana.26802\u003c/span\u003e\u003cspan address=\"10.1002/ana.26802\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNie X, Liu J, Yan B, Ng FC, Liu S, Wang Y, Wang M, Zheng L, Wang Z, Wang Y et al (2025) Cerebral Edema Progression and Outcomes in Large Infarct Patients Undergoing Endovascular Thrombectomy. Ann Neurol. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ana.27235\u003c/span\u003e\u003cspan address=\"10.1002/ana.27235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBai J, Lyden PD (2015) Revisiting cerebral postischemic reperfusion injury: new insights in understanding reperfusion failure, hemorrhage, and edema. Int J Stroke 10:143\u0026ndash;152. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/ijs.12434\u003c/span\u003e\u003cspan address=\"10.1111/ijs.12434\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo W, Xu J, Zhao W, Zhang M, Ma J, Chen J, Duan J, Ma Q, Song H, Li S et al (2022) A nomogram for predicting malignant cerebral artery infarction in the modern thrombectomy era. Front Neurol 13:934051. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2022.934051\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2022.934051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"midline shift, thrombectomy, malignant cerebral edema, diffusion-weighted imaging, radiomics","lastPublishedDoi":"10.21203/rs.3.rs-7191348/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7191348/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eEarly and severe (ES) midline shift (MLS ≥10 mm) simultaneously occurring within 24 hours after endovascular thrombectomy (EVT) is a life-threatening emergency that requires immediate intervention. This study aims to describe ES-MLS and develop a predictive model in anterior circulation occlusion who have undergone EVT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis retrospective cohort study utilized data from a prospective registry. Functional outcome was defined as a modified Rankin Scale score of 0–2. Radiomic features extracted from pre-EVT diffusion-weighted imaging were subjected to LASSO regression with fourfold cross-validation. Clinical features were selected via multivariable regression and integrated into a nomogram, with performance evaluated through receiver operating characteristic curve analysis in both training and validation datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 481 patients (median age 68 [IQR 58–76], 39.7% female) were included in this study, which consisted of a training dataset (n = 361) and a validation dataset (n = 120). In the ES-MLS group, 85.7% had died and none had a functional outcome at the 90-day follow-up. Recanalization, NIHSS score, and two radiomic features were identified as factors associated with ES-MLS in the nomogram. The predictive model exhibited an area under the curve (AUC) of 0.844 (95% confidence interval [CI], 0.803–0.880) in the training dataset and 0.823 (95% CI, 0.743–0.887) in the validation dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis is the initial structured overview of ES-MLS after EVT, featuring a model designed for personalized prediction of ES-MLS. The tool may enhance patient selection before EVT and refine the aggressive monitoring strategy after EVT.\u003c/p\u003e","manuscriptTitle":"Early Severe Midline Shift in Acute Ischemic Stroke Following Endovascular Thrombectomy Within 24 Hours","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 10:32:34","doi":"10.21203/rs.3.rs-7191348/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e21722b-3f2d-428b-95c6-3c25a0aca910","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T16:03:13+00:00","versionOfRecord":{"articleIdentity":"rs-7191348","link":"https://doi.org/10.1007/s00234-025-03818-4","journal":{"identity":"neuroradiology","isVorOnly":false,"title":"Neuroradiology"},"publishedOn":"2025-11-07 15:58:02","publishedOnDateReadable":"November 7th, 2025"},"versionCreatedAt":"2025-07-31 10:32:34","video":"","vorDoi":"10.1007/s00234-025-03818-4","vorDoiUrl":"https://doi.org/10.1007/s00234-025-03818-4","workflowStages":[]},"version":"v1","identity":"rs-7191348","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7191348","identity":"rs-7191348","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}