Objective To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated ( p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

No abstract

This study aimed to evaluate the difference in wall shear stress (WSS) (axial, circumferential, and 3D) between high-risk and low-risk plaques in patients with moderate carotid artery stenosis and to identify which time points and directions play the dominant roles in determining the risk associated with plaques. Forty carotid arteries in 30 patients were examined in this study. All patients underwent high-resolution vessel wall (HRVW) imaging, diffusion-weighted imaging (DWI), and 4D flow MRI; HRVW imaging and DWI were used to separate low- and high-risk plaque. Twenty-four high-risk plaques and 16 low-risk plaques were enrolled. An independent-sample t-test was used to compare WSS between low- and high-risk plaques in the whole cardiac cycle and at 20 different time points in the cardiac cycle. The study found that patients with high-risk plaques had higher WSS than those with low-risk plaques throughout the entire cardiac cycle (p < 0.05), but the changes varied at the 20 different time points. The number of non-significant differences (p > 0.05) was less in diastole than in systole across different time points. The axial WSS values were higher than the circumferential WSS values; the difference in axial WSS values between high- and low-risk plaques was more significant than the difference in circumferential WSS, whereas 3D WSS values best reflected the difference between high-risk and low-risk plaques because they showed significant differences at every time point. In conclusion, increased WSS, especially during the diastolic period and in the axial direction, may be a signal of a high-risk plaque and may cause cerebrovascular events in patients with moderate carotid artery stenosis. Additionally, WSS can provide hemodynamic information and help clinicians make more appropriate decisions for patients with plaques.

ObjectiveThis study aimed to construct a radiomics-based MRI sequence from high-resolution magnetic resonance imaging (HRMRI), combined with clinical high-risk factors for non-invasive differentiation of the plaque of symptomatic patients from asyptomatic patients.MethodsA total of 115 patients were retrospectively recruited. HRMRI was performed, and patients were diagnosed with symptomatic plaques (SPs) and asymptomatic plaques (ASPs). Patients were randomly divided into training and test groups in the ratio of 7:3. T2WI was used for segmentation and extraction of the texture features. Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were employed for the optimized model. Radscore was applied to construct a diagnostic model considering the T2WI texture features and patient demography to assess the power in differentiating SPs and ASPs.ResultsSPs and ASPs were seen in 75 and 40 patients, respectively. Thirty texture features were selected by mRMR, and LASSO identified a radscore of 16 radiomics features as being related to plaque vulnerability. The radscore, consisting of eight texture features, showed a better diagnostic performance than clinical information, both in the training (area under the curve [AUC], 0.923 vs. 0.713) and test groups (AUC, 0.989 vs. 0.735). The combination model of texture and clinical information had the best performance in assessing lesion vulnerability in both the training (AUC, 0.926) and test groups (AUC, 0.898).ConclusionThis study demonstrated that HRMRI texture features provide incremental value for carotid atherosclerotic risk assessment.

No abstract

Background Diffusion‐weighted imaging (DWI) is a useful technique to detect pancreatic lesion. In DWIs, field‐of‐view optimized and constrained undistorted single‐shot (FOCUS) can improve the spatial resolution and multiplexed sensitivity‐encoding (MUSE) can gain a high signal‐to‐noise ratio (SNR). Based on the advantage of FOCUS and MUSE, a new DWI sequence—named FOCUS‐MUSE DWI (FOCUS combined with MUSE)—was developed to delineate the pancreas. Purpose To investigate the reliability of FOCUS‐MUSE DWI compared to FOCUS, MUSE and single‐shot (SS) DWI via the systematical evaluation of the apparent diffusion coefficient (ADC) measurements, SNR and image quality. Study Type Prospective. Subjects A total of 33 healthy volunteers and 9 patients with pancreatic lesion. Field Strength/Sequence A 3.0 T scanner. FOCUS‐MUSE DWI, FOCUS DWI, MUSE DWI, SS DWI. Assessment For volunteers, ADC and SNR were measured by two readers in the pancreatic head, body, and tail. For all subjects, the diagnostic image quality score was assessed by three other readers on above four DWIs. Statistical Tests Paired‐sample T‐test, intraclass correlation (ICC), Bland–Altman method, Friedman test, Dunn‐Bonferroni post hoc test and kappa coefficient. A significance level of 0.05 was used. Results FOCUS‐MUSE DWI had the best intersession repeatability of ADC measurements (head: 59.53, body: 101.64, tail: 42.30) among the four DWIs, and also maintained the significantly highest SNR (reader 1 [head: 19.68 ± 3.23, body: 23.42 ± 5.00, tail: 28.85 ± 4.96], reader 2 [head: 19.93 ± 3.52, body: 23.02 ± 5.69, tail: 29.77 ± 6.33]) except for MUSE DWI. Furthermore, it significantly achieved better image quality in volunteers (median value: 4 score) and 9 patients (most in 4 score). Data Conclusion FOCUS‐MUSE DWI improved the reliability of pancreatic images with the most stable ADC measurement, best image quality score and sufficient SNR among four DWIs. Evidence Level 2 Technical Efficacy Stage 2

Objectives This study aimed to evaluate the feasibility of reduced full-of-view synthetic high-b value diffusion-weighted images (rFOV-syDWIs) in the clinical application of cervical cancer based on image quality and diagnostic efficacy. Methods We retrospectively evaluated the data of 35 patients with cervical cancer and 35 healthy volunteers from May to November 2021. All patients and volunteers underwent rFOV-DWI scans, including a 13b-protocol: b = 0, 25, 50, 75, 100, 150, 200, 400, 600, 800, 1000, 1200, and 1500 s/mm2 and a 5b-protocol: b = 0, 100, 400, 800,1500 s/mm2. rFOV-syDWIs with b values of 1200 (rFOV-syDWIb=1200) and 1500 (rFOV-syDWIb=1500) were generated from two different multiple-b-value image datasets using a mono-exponential fitting algorithm. According to homoscedasticity and normality assessed by the Levene’s test and Shapiro–Wilk test, the inter-modality differences of quantitative measurements were, respectively, examined by Wilcoxon signed-rank test or paired t test and the inter-group differences of ADC values were examined by independent t test or Mann–Whitney U test. Results A higher inter-reader agreement between SNRs and CNRs was found in 13b-protocol and 5b-protocol rFOV-syDWIb=1200/1500 compared to 13b-protocol rFOV-sDWIb=1200/1500 (p < 0.05). AUC of 5b-protocol syADCmean,b=1200/1500 and syADCminimum,b=1200/1500 was equal or higher than that of 13b-protocol sADCmean,b=1200/1500 and sADCminimum,b=1200/1500. Conclusions rFOV-syDWIs provide better lesion clarity and higher image quality than rFOV-sDWIs. 5b-protocol rFOV-syDWIs shorten scan time, and synthetic ADCs offer reliable diagnosis value as scanned 13b-protocol DWIs.

Purpose To evaluate the feasibility of histogram analysis of T2* value for the detection and grading of degenerative lumbar intervertebral discs (IVDs) and for the characterization of microstructural heterogeneity of discs. Methods Two hundred fourteen lumbar IVDs of 44 subjects with chronic low back pain were examined using sagittal T2WI and axial T2* mapping. All IVDs were classified according to the Pfirrmann grade on T2WI. The correlations between histogram-derived parameters based on T2* values (T2*-HPs) of IVDs and Pfirrmann grade as well as between "red zone ratio" (area of "red zone" on T2* color maps over cross-sectional area of corresponding IVDs) and Pfirrmann grade were calculated. ResultsThe agreement for Pfirrmann grade of IVDs was excellent (κ = 0.808, P < 0.001). The consistency of the measured T2*-HPs was excellent, with ICCs ranging from 0.828-0.960. Each histogram-derived parameter had a statistically significant relationship with Pfirrmann grade (P < 0.001). The bright "red zone" on T2* color maps of IVDs displayed as a separated peak relative to the rest of voxels in histograms. The mean area ratio of "red zone" over the corresponding IVD was 9.234% ± 6.680 and ranged from 0.517% to 30.598%. The "red zone ratio" was highly related to Pfirrmann grade (r = − 0.732, P < 0.001). Conclusion Histogram analysis of T2* value is an effective tool for the detection and grading of degenerative IVDs. Identification of the "red zone" may provide new breakthroughs in the study of disc degeneration initiation and generate new hypotheses in anatomical and histological studies of IVDs.