A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study.

OBJECTIVES: We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making. MATERIALS AND METHODS: This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models. RESULTS: Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model. CONCLUSIONS: The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients. CLINICAL RELEVANCE STATEMENT: The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS. KEY POINTS: Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.

Clot-based time attenuation curve as a novel imaging predictor of mechanical thrombectomy functional outcome in acute ischemia stroke.

OBJECTIVES: To investigate whether a novel assessment of thrombus permeability obtained from perfusion computed tomography (CTP) can act as a more accurate predictor of clinical response to mechanical thrombectomy (MT) in acute ischemic stroke (AIS). MATERIALS AND METHODS: We performed a study including two cohorts of AIS patients who underwent MT admitted to a single-center between April 2018 and February 2022: a retrospective development cohort (n = 71) and a prospective independent validation cohort (n = 96). Thrombus permeability was determined in terms of entire thrombus time-attenuation curve (TAC) on CTP. Association between thrombus TAC distributions and histopathological results was analyzed in the development cohort. Logistic regression was used to assess the performance of the TAC for predicting 90-day modified Rankin Scale (mRS) score, and good outcome was defined as a mRS score of ≤ 2. Basic clinical characteristics was used to build a routine clinical model. A combined model gathered TAC and basic clinical characteristics was also developed. The performance of the three models is compared on the independent validation set. RESULTS: Two TAC distributions were observed-unimodal (uTAC) and linear (lTAC). TAC distributions achieved strong correlations (|r|= 0.627, p < 0.001) with histopathological results, in which uTAC associated with fibrin- and platelet-rich clot while lTAC associated with red blood cell-rich clot. The uTAC was independently associated with poor outcome (odds ratio, 0.08 [95% confidence interval (CI), 0.02-0.31]; p < 0.001). TAC distributions yielded an AUC of 0.78 (95% CI, 0.70-0.87) for predicting clinical outcome. When combined clinical characteristics, the performance was significantly improved (AUC, 0.85 [95% CI, 0.76-0.93]; p < 0.001) and higher than routine clinical model (AUC, 0.69 [95% CI, 0.59-0.83]; p < 0.001). CONCLUSIONS: Thrombus TAC on CTP were found to be a promising new imaging biomarker to predict the outcomes of MT in AIS. CLINICAL RELEVANCE STATEMENT: This study revealed that clot-based time attenuation curve based on admission perfusion CT could reflect the permeability and composition of thrombus and, also, provide valuable information to predict the clinical outcomes of mechanical thrombectomy in patients with acute ischemia stroke. KEY POINTS: • Two time-attenuation curves distributions achieved strong correlations (|r|= 0.627, p < 0.001) with histopathological results. • The unimodal time-attenuation curve was independently associated with poor outcome (odds ratio, 0.08 [0.02-0.31]; p < 0.001). • The time-attenuation curve distributions yielded a higher performance for detecting clinical outcome than routine clinical model (AUC, 0.78 [0.70-0.87] vs 0.69 [0.59-0.83]; p < 0.001).

Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study.

OBJECTIVES: To develop a clot-based radiomics model using CT imaging radiomic features and machine learning to identify cardioembolic (CE) stroke before mechanical thrombectomy (MTB) in patients with acute ischemic stroke (AIS). MATERIALS AND METHODS: This retrospective four-center study consecutively included 403 patients with AIS who sequentially underwent CT and MTB between April 2016 and July 2021. These were grouped into training, testing, and external validation cohorts. Thrombus-extracted radiomic features and basic information were gathered to construct a machine learning model to predict CE stroke. The radiological characteristics and basic information were used to build a routine radiological model. A combined radiomics and radiological features model was also developed. The performances of all models were evaluated and compared in the validation cohort. A histological analysis helped further assess the proposed model in all patients. RESULTS: The radiomics model yielded an area under the curve (AUC) of 0.838 (95% confidence interval [CI], 0.771-0.891) for predicting CE stroke in the validation cohort, significantly higher than the radiological model (AUC, 0.713; 95% CI, 0.636-0.781; p = 0.007) but similar to the combined model (AUC, 0.855; 95% CI, 0.791-0.906; p = 0.14). The thrombus radiomic features achieved stronger correlations with red blood cells (|rmax|, 0.74 vs. 0.32) and fibrin and platelet (|rmax|, 0.68 vs. 0.18) than radiological characteristics. CONCLUSION: The proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method. KEY POINTS: • Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis. • The proposed CT imaging-based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007). • Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |rmax|, 0.74; fibrin and platelet, |rmax|, 0.68) than routine radiological characteristics.

MR image-based radiomics to differentiate type Ι and type ΙΙ epithelial ovarian cancers.

OBJECTIVES: Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC. METHODS: In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation. RESULTS: The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection. CONCLUSIONS: MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis. KEY POINTS: • Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.

MRI-Based Machine Learning for Differentiating Borderline From Malignant Epithelial Ovarian Tumors: A Multicenter Study.

BACKGROUND: Preoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results. PURPOSE: To develop and validate an objective MRI-based machine-learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation. STUDY TYPE: Retrospective study of eight clinical centers. POPULATION: In all, 501 women with histopathologically-confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159). FIELD STRENGTH/SEQUENCE: Preoperative MRI within 2 weeks of surgery. Single- and multiparameter (MP) machine-learning assessment models were built utilizing the following four MRI sequences: T2 -weighted imaging (T2 WI), fat saturation (FS), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast-enhanced (CE)-T1 WI. ASSESSMENT: Diagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early-stage MEOT was made. Six radiologists of varying experience also interpreted the MR images. STATISTICAL TESTS: Mann-Whitney U-test: significance of the clinical characteristics; chi-square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC). RESULTS: The MP-ST model performed better than the MP-WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early-stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679-0.924) and external (mean AUC = 0.797; range, 0.744-0.867) validation cohorts. DATA CONCLUSION: Performance of the MRI-based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women. LEVEL OF EVIDENCE: Level 4. TECHNICAL EFFICACY: Stage 2. J. Magn. Reson. Imaging 2020;52:897-904.

Metabolic Changes in the Bilateral Visual Cortex of the Monocular Blind Macaque: A Multi-Voxel Proton Magnetic Resonance Spectroscopy Study.

The metabolic changes accompanied with adaptive plasticity in the visual cortex after early monocular visual loss were unclear. In this study, we detected the metabolic changes in bilateral visual cortex of normal (group A) and monocular blind macaque (group B) for studying the adaptive plasticity using multi-voxel proton magnetic resonance spectroscopy (1H-MRS) at 32 months after right optic nerve transection. Then, we compared the N-Acetyl aspartate (NAA)/Creatine (Cr), myoinositol (Ins)/Cr, choline (Cho)/Cr and Glx (Glutamate + glutamine)/Cr ratios in the visual cortex between two groups, as well as between the left and right visual cortex of group A and B. Compared with group A, in the bilateral visual cortex, a decreased NAA/Cr and Glx/Cr ratios in group B were found, which was more clearly in the right visual cortex; whereas the Ins/Cr and Cho/Cr ratios of group B were increased. All of these findings were further confirmed by immunohistochemical staining. In conclusion, the difference of metabolic ratios can be detected by multi-voxel 1H-MRS in the visual cortex between groups A and B, which was valuable for investigating the adaptive plasticity of monocular blind macaque.

Radiomics of intrathrombus and perithrombus regions for Post-EVT intracranial hemorrhage risk Prediction: A multicenter CT study.

OBJECTIVES: This study aimed to assess the predictive performance of radiomics derived from computed tomography (CT) images of thrombus regions in predicting the risk of intracranial hemorrhage (ICH) following endovascular thrombectomy (EVT). MATERIALS AND METHODS: This retrospective multicenter study included 336 patients who underwent admission CT and EVT for acute anterior-circulation large vessel occlusion between December 2018 and December 2023. Follow-up imaging was performed 24 h post-procedure to evaluate the occurrence of ICH. 230 patients from centers A and B were randomly allocated into training and test groups in a 7:3 ratio, while the remaining 106 patients from center C comprised the validation cohort. Radiologists manually segmenting the thrombus on CT images, and the perithrombus region was defined by expanding the initial region of interest (ROI). A total of 428 radiomics features were extracted from both intrathrombus and perithrombus regions on CT images. The Mann-Whitney U test was used for feature selection, and least absolute shrinkage and selection operator (LASSO) regression was employed for model development, followed by validation using a 5-fold cross-validation approach. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). RESULTS: Among the eligible patients, 128 (38.1 %) experienced ICH after EVT. The combined model exhibited superior performance in the training cohort (AUC: 0.913, 95 % CI: 0.861-0.965), test cohort (AUC: 0.868, 95 % CI: 0.775-0.962), and validation cohort (AUC: 0.850, 95 % CI: 0.768-0.912). Notably, in the validation group, both the perithrombus and combined models demonstrated higher predictive accuracy compared to the intrathrombus model (0.837 vs. 0.684, p = 0.02; AUC: 0.850 vs. 0.684, p = 0.01). CONCLUSIONS: Radiomics features derived from the perithrombus region significantly enhance the prediction of ICH after EVT, providing valuable insights for optimizing post-procedural clinical decisions. CLINICAL RELEVANCE STATEMENT: This study highlights the importance of radiomics extracted from intrathrombus and perithrombus region in predicting intracranial hemorrhagefollowing endovascular thrombectomy, which can aid in improving patient outcomes.

The denitrification characteristics of Na2S2O8 solution in a falling film reactor.

Wet scrubbing technology is an effective emission control technology for marine diesel engines. Nitric oxide (NO) is one of the main component of ship emissions, the sodium persulfate (Na2S2O8) can facilitate the NO mass transfer process to a rapid reaction. Falling film reactors are widely used in rapid gas-liquid reactions, however, the reaction characteristics of denitrification using Na2S2O8 solution in a falling film reactor are not clear, which were investigated in this paper. The factors of NO mass transfer flux were tested with the liquid-gas ratio of 15 L/m3. The effects of solution properties and temperatures on the reaction driving force were studied by calculating the chemical reaction equilibrium constants and Gibbs free energy changes. The results showed that the NO mass transfer flux increased with the increase of temperature, Na2S2O8 concentration, O2 concentration and NO concentration. NO mass transfer flux increased by 41.00% and then decreased by 2.12% as the pH value increased from 7 to 10 and then rising to 12. The Gibbs free energy changes of alkaline solutions were 114.22%-130.99% lower than those of acidic solution at 303-343 K, and the chemical reaction equilibrium constants were higher. Na2S2O8/seawater system has great application potential in marine exhaust gas purification.

CT-based Intra-thrombus and Peri-thrombus Radiomics for Prediction of Prognosis After Endovascular Thrombectomy: A Retrospective Study Across Two Centers.

BACKGROUND AND PURPOSE: Complications from endovascular thrombectomy (EVT) can negatively affect clinical outcomes, making the development of a more precise and objective prediction model essential. This research aimed to assess the effectiveness of radiomic features derived from pre-surgical CT scans in predicting the prognosis post- EVT in acute ischemic stroke patients. MATERIALS AND METHODS: This investigation included 336 acute ischemic stroke patients from two medical centers, spanning from March 2018 to March 2024. The participants were split into a training cohort of 161 patients and a validation cohort of 175 patients. Patient outcomes were rated with the mRS: 0-2 for good, 3-6 for poor. A total of 428 radiomic features were derived from intra-thrombus and peri-thrombus regions in non-contrast CT and CT angiography images. Feature selection was conducted using a least absolute shrinkage and selection operator regression model. The efficacy of eight different supervised learning models was assessed using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: Among all models tested in the validation cohort, the logistic regression algorithm for combined model achieved the highest AUC (0.87, with a 95% confidence interval of 0.81 to 0.92), outperforming other algorithms. The combined use of radiomic features from both the intra-thrombus and peri-thrombus regions significantly enhanced diagnostic accuracy over models using features from a single region (0.81 vs 0.70, 0.77), highlighting the benefit of integrating data from both regions for improved prediction. CONCLUSIONS: The findings suggest that a combined radiomics model based on CT imaging serves as a potent approach to assessing the prognosis following EVT. The logistic regression model, in particular, proved to be both effective and stable, offering critical insights for the management of stroke. ABBREVIATIONS: AUC=area under the curve; EVT=endovascular thrombectomy; KNN=k-nearest neighbors; LASSO=least absolute shrinkage and selection operator; LightGBM=Light Gradient Boosting Machine; LR=logistic regression; MLP=multi-layer perceptron; RF=random forest; SVM=support vector machine; XGBoost=extreme gradient boosting.

Synchronous Superficial Middle Cerebral Vein Outflow Correlates Favorable Tissue Fate After Mechanical Thrombectomy for Acute Ischemic Stroke.

RATIONALE AND OBJECTIVES: The purpose of this study was to determine the association between hemispheric synchrony in venous outflow at baseline and tissue fate after mechanical thrombectomy (MT) for acute ischemic stroke (AIS). MATERIALS AND METHODS: A two-center retrospective analysis involving AIS patients who underwent MT was performed. The four cortical veins of interest include the superficial middle cerebral vein (SMCV), sphenoparietal sinus (SS), vein of Labbé (VOL), and vein of Trolard (VOT). Baseline computed tomography perfusion data were used to compare the following outflow parameters between the hemispheres: first filling time (△FFT), time to peak (△TTP) and total filling time (△TFT). Synchronous venous outflow was defined as △FFT = 0. Multivariable regression analyses were performed to evaluate the association of venous outflow synchrony with penumbral salvage, infarct growth, and intracranial hemorrhage (ICH) after MT. RESULTS: A total of 151 patients (71.4 ± 13.2 years, 65.6% women) were evaluated. Patients with synchronous SMCV outflow demonstrated significantly greater penumbral salvage (41.3 mL vs. 33.1 mL, P = 0.005) and lower infarct growth (9.0 mL vs. 14.4 mL, P = 0.015) compared to those with delayed SMCV outflow. Higher △FFTSMCV (ß = -1.44, P = 0.013) and △TTPSMCV (ß = -0.996, P = 0.003) significantly associated with lower penumbral salvage, while higher △FFTSMCV significantly associated with larger infarct growth (ß = 1.09, P = 0.005) and increased risk of ICH (odds ratio [OR] = 1.519, P = 0.047). CONCLUSION: Synchronous SMCV outflow is an independent predictor of favorable tissue outcome and low ICH risk, and thereby carries the potential as an auxiliary radiological marker aiding the treatment planning of AIS patients.

Boosting Deep Learning for Interpretable Brain MRI Lesion Detection through the Integration of Radiology Report Information.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To guide a deep learning (DL) model's attention toward brain lesion MRI characteristics by incorporating radiology report-derived textual features to achieve interpretable lesion detection. Materials and Methods In this retrospective study, 35282 brain MRI scans (January 2018-June 2023) and corresponding radiology reports from center 1 were used for training, validation, and internal testing. 2655 brain MRI scans (January 2022-December 2022) from centers 2-5 were reserved for external testing. Textual features were extracted from radiology reports to guide a DL model (ReportGuidedNet) focusing on lesion characteristics. Another DL model (PlainNet) without textual features was developed for comparative analysis. Both models diagnosed 15 conditions, including 14 diseases and normal brains. Performance of each model was assessed by calculating macro-and microaveraged area under the receiver operating characteristic curves (ma-AUC, mi-AUC). Attention maps, visualizing model attention, were assessed with a 5-point Likert scale. Results ReportGuidedNet outperformed PlainNet for all diagnoses on both internal (ma-AUC: 0.93 [95% CI: 0.91- 0.95] versus 0.85 [95% CI: 0.81-0.88]; mi-AUC: 0.93 [95% CI: 0.90-0.95] versus 0.89 [95% CI: 0.83-0.92]) and external (ma-AUC: 0.91 [95% CI: 0.88-0.93] versus 0.75 [95% CI: 0.72-0.79]; mi-AUC: 0.90 [95% CI: 0.87-0.92] versus 0.76 [95% CI: 0.72-0.80]) testing sets. The performance difference between internal and external testing sets was smaller for ReportGuidedNet than for PlainNet (Δma-AUC: 0.03 versus 0.10; Δmi-AUC: 0.02 versus 0.13). The Likert scale score of ReportGuidedNet was higher than that of PlainNet (mean ± SD: 2.50 ± 1.09 versus 1.32 ± 1.20; P < .001). Conclusion The integration of radiology report textual features improved the DL model's ability to detect brain lesions, enhancing interpretability and generalizability. Published under a CC BY 4.0 license.

The Value of Dual-Energy Computed Tomography Angiography-Derived Parameters in the Evaluation of Clot Composition.

OBJECTIVES: We aimed to assess the value of dual-energy computed tomography angiography (DE-CTA) derived parameters as a quantitative biomarker of thrombus composition in acute ischemic stroke (AIS). METHODS: AIS patients who underwent DE-CTA before thrombectomy between August 2016 and September 2022 were included in this study. We assessed the relative proportion of red blood cells (RBCs) and the fibrin/platelet ratio (F/P) of the retrieved clots and categorized the clots as RBC-dominant (RBCs > F/P) or F/P-dominant (F/P > RBCs). The thrombus based parameters were measured on polyenergetic images (PEI), virtual monoenergetic (VM), virtual non-contrast (VNC), iodine concentration (IC), and effective atomic number (Zeff) images respectively, and the slope of the spectral Hounsfield unit curve (λHU) was calculated. These parameters were compared in the DE-CTA images of RBC- and F/P-dominant thrombi. The diagnostic performance of the parameters was analyzed using the ROC curve. Correlations between thrombus composition and DE-CTA-derived parameters were assessed. RESULTS: The retrieved clots in 54 of 88 patients (61.36%) were RBC-dominant. The RBC-dominant thrombi showed significantly higher VNC values and lower IC, λHU, and Zeff values than the F/P-dominant thrombi (p < 0.05). The CT density measured on IC images showed the largest AUC value (AUC, 0.94; sensitivity, 77.78%; specificity, 100.00%). The Spearman rank-order correlation coefficient values showed that CT density measured on IC images of the thrombus showed the strongest association with the proportion of RBCs (r = -0.64, p < 0.001) and F/P (r = 0.65, p < 0.001). CONCLUSIONS: DE-CTA-derived parameters, especially the CT density measured on IC images, could be associated with thrombus composition and allow for personalized thrombectomy strategies.

Computed Tomography Angiography-Based Thrombus Radiomics for Predicting the Time Since Stroke Onset.

RATIONALE AND OBJECTIVES: The measurement of the time since stroke onset (TSS) is crucial for decision-making in the treatment of acute ischemic stroke (AIS). This study assessed the utility of computed tomography angiography (CTA) radiomics features (RFs) to estimate TSS. MATERIALS AND METHODS: A total of 221 patients with AIS were enrolled in this retrospective study and were divided into a training group (n = 154) and a test group (n = 67). Thrombi in CTA images were manually outlined using ITK-SNAP. Images were aligned, normalized, and pre-processed to extract RFs. The TSS was calculated as the time from stroke onset to CTA completion. The patients were classified into two groups according to estimated TSS: ≤4.5 and >4.5 hours. A total of 944 RFs were extracted from CTA images. Clinical factors associated with TSS were identified using multivariate logistic regression, and a combined model (clinical data and RFs) was constructed. The predictive value of the models was assessed by the area under the receiver operating characteristic curve (AUC). The performance of the models was compared using the DeLong test, and clinical utility was evaluated by decision curve analysis. RESULTS: The AUC of the radiomics model was 0.803 (95% confidence interval [CI]: 0.733-0.873) and 0.803 (95% CI: 0.698-0.908) in the training and test cohorts, respectively. The AUC of the combined model (containing data on age, diabetes, and atrial fibrillation) in the training and test sets was 0.813 (95% CI: 0.750-0.889) and 0.803 (95% CI: 0.699-0.907), respectively. The DeLong test showed no significant difference between the radiomics and combined models. Decision curve analysis showed that both models had clinical utility. CONCLUSION: CTA-based thrombus radiomics can estimate TSS in patients with AIS. The addition of clinical data to the model does not improve predictive performance.

The Influence of Perceived External Prestige on Emotional Labor of Frontline Employees: The Mediating Roles of Organizational Identification and Impression Management Motive.

Drawing on both the organization identification and impression management theories, we propose that perceived external prestige of frontline employees influences their emotional labor through organizational identification and impression management motive. Further, the relative influence of either pathway depends upon perceived organizational support. Using survey data from 377 frontline employees in 104 hotels, the results indicate that perceived external prestige is positively related to deep acting, and negatively related to surface acting. Organizational identification partially mediates the relationship between perceived external prestige and deep acting. However, the relationship between perceived external prestige and surface acting is partially mediated both by organizational identification and impression management motive. In addition, perceived organizational support positively moderates the relationship between perceived external prestige and organizational identification, and negatively moderates the relationship between perceived external prestige and impression management motive, respectively.

Dysfunctional customer behavior influences on employees' emotional labor: The moderating roles of customer orientation and perceived organizational support.

Despite increasing interest being given to dysfunctional customer behavior in multiple service sectors, it is unclear how and why different types of dysfunctional customer behavior (verbal abuse, disproportionate demand, and illegitimate complaint) affect frontline employees' emotional labor during the service interactions. Drawing upon the conservation of resources theory, we propose a conceptual model in which verbal abuse, disproportionate demand, and illegitimate complaint differentially influence frontline employees' emotional labor strategies (surface acting and deep acting). Further, the boundary conditions of these relationships are considered by introducing perceived organizational support and customer orientation as moderators. Using survey data from 436 frontline employees of five call centers in China, hypotheses were tested through a hierarchical regression analysis. The results indicated that verbal abuse and illegitimate complaint exerted positive effects on surface acting. Particularly, these positive effects were weaker when frontline employees perceived organizational support was high. Also, verbal abuse's positive effect on surface acting was weaker when frontline employees' customer orientation was high. Customer's verbal abuse, disproportionate demand, and illegitimate complaint negatively influenced frontline employees' deep acting. The negative effect of disproportionate demand on deep acting was weaker when perceived organizational support was high. However, when frontline employees' customer orientation was high, the negative effects of disproportionate demand and illegitimate complaints on deep acting were weaker.

Assessment of Primary Collateral Grades Based on the Integrity of Willis' Circle: Predicting the Prognosis of Acute Intracranial Internal Carotid Artery Occlusion Before Thrombectomy.

OBJECTIVE: Endovascular mechanical thrombectomy (EVMT) has shown significant efficacy in improving neurological functions in patients with intracranial internal carotid artery occlusion (IICAO), but its clinical outcomes are variable. We examined the relationship between favorable clinical outcomes after EVMT in IICAO and a set of predictors. METHODS: In this retrospective study, 189 patients with IICAO treated by EVMT at 3 centers from November 2015 to December 2020 were included and analyzed. Non-contrast computed tomography and computed tomography angiography were evaluated on admission. The morphology of IICAO was categorized into Ia, Ib, L, or T types, depending on the involvement of the posterior communicating artery origin, proximal anterior cerebral artery, and middle cerebral artery. The Willis' circle was categorized as integrated or compromised Willis' circle. In combination with the involvement of the IICAO and the integrity of Willis' circle, we used the primary collateral grade (PCG) to describe the presence of functional Willisian collaterals. Baseline data including demographics, characteristics, vascular risk factors, and initial National Institutes of Health Stroke Scale scores were collected. Hemorrhagic transformation was evaluated using the 24-hour non-contrast computed tomography after EVMT. Favorable outcomes based on modified Rankin scale, were defined as 0-2 at 90 days. RESULTS: A total of 189 patients were included (median age, 69 years; 126 male [66.7%]). 104 patients [55.0%] showed reperfusion after EVMT, but 72 patients [38.1%] achieved favorable outcomes at 90 days. The mortality rate of type Ib was significantly higher than that with type Ia (χ2 = 14.21, P = 0.001). The outcome with different structure of Willis' circle was not statistically different between the 2 groups. A multivariate logistic regression analysis showed that IICAO T-type (odds ratio, 0.028 [95% confidence interval: 0.323-3.829], P = 0.042) and PCG 2 (odds ratio 9.427[95% confidence interval:1.863-47.698], P = 0.007) were predictors of favorable outcomes. CONCLUSIONS: Evaluation of PCG by determining the type of IICAO and the integrity of Willis' circle may serve as a valuable indicator for the prognosis and as an essential reference for screening patients before EVMT.

A Fibrinogen-Mimicking, Activated-Platelet-Sensitive Nanocoacervate Enhances Thrombus Targeting and Penetration of Tissue Plasminogen Activator for Effective Thrombolytic Therapy.

The development of a fibrinolytic system with long circulation time, high thrombus targeting, efficient thrombus penetration, effective thrombolysis, and minimal hemorrhagic risk remains a major challenge. Herein, inspired by fibrinogen binding to activated platelets in thrombosis, this article reports a fibrinogen-mimicking, activated-platelet-sensitive nanocoacervate to enhance thrombus penetration of tissue plasminogen activator (tPA) for targeted thrombolytic therapy. This biomimetic nanothrombolytic system, denoted as RGD-Chi@tPA, is constructed by "one-pot" coacervation through electrostatic interactions between positively charged arginine-glycine-aspartic acid (RGD)-grafted chitosan (RGD-Chi) and negatively charged tPA. Flow cytometry and confocal laser scanning microscopy measurements show targeting of RGD-Chi@tPA to activated platelets. Controlled tPA release triggered by activated platelets at a thrombus site is demonstrated. Its targeted fibrinolytic and thrombolytic activities are measured in in vitro models. The pharmacokinetic profiles show that RGD-Chi@tPA can significantly prolong circulation time compared to free tPA. In a mouse tail thrombus model, RGD-Chi@tPA displays efficient thrombus targeting and penetration, enabling a complete vascular recanalization as confirmed by the fluorescence imaging, histochemical assay, and laser speckle contrast imager. Consequently, RGD-Chi@tPA induces a substantial enhancement in thrombolysis with minimal hemorrhagic risk compared to free tPA. This simple, effective, and safe platform holds great promise for the development of thrombolytic nanomedicines.

Differentiating between benign and malignant sinonasal lesions using dynamic contrast-enhanced MRI and intravoxel incoherent motion.

PURPOSE: To explore the value of dynamic contrast-enhanced MRI (DCE-MRI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant sinonasal lesions and investigate the correlations between the two methods. METHODS AND MATERIALS: Patients with sinonasal lesions (42 benign and 31 malignant) who underwent DCE-MRI and IVIM before confirmation by histopathology were enrolled in this prospective study. Parameters derived from DCE-MRI and IVIM were measured, the optimal cut-off values for differential diagnosis were determined, and the correlations between the two methods were evaluated. Statistical analyses were performed using the Wilcoxon rank sum test, receiver operating characteristic (ROC) curve analysis, and Spearman's rank correlation. RESULTS: Significantly higher Ktrans and Kep values but lower D and f values were found in malignant lesions than in benign lesions (all p<0.001). There were no significant differences in the Ve and D* values between the two groups. The area under the curve (AUC) of Ktrans was significantly higher than those of other parameters. There was no significant difference between the AUCs of DCE-MRI and IVIM with parameters combined (p=0.86). Significant inverse but weak correlations were found between D and Ktrans (r=-0.46, p<0.001), f and Ktrans (r=-0.41, p<0.001), D and Kep (r=-0.37, p=0.008), and f and Kep (r=-0.33, p=0.004). CONCLUSIONS: DCE-MRI and IVIM can effectively differentiate between benign and malignant sinonasal lesions. IVIM findings correlate with DCE-MRI results and may represent an alternative to DCE-MRI.