Zhifeng Liu

Objective: To investigate the utility of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) for early detection of renal injury 24 h after cardiac arrest and cardiopulmonary resuscitation. Materials and methods: Twenty-four male Sprague–Dawley rats were randomized into cardiac arrest ( n = 15) and sham-operated ( n = 9) groups. Five cardiac arrest rats died during the procedure, resulting in 10 cardiac arrest and 9 sham rats for analysis. IVIM-MRI and renal parameters were assessed 24 h after cardiac arrest and cardiopulmonary resuscitation. Diffusion-related parameters, including apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and exponential ADC (eADC), were obtained. The primary imaging endpoint was defined as D. Following completion of MRI, renal function, histopathology, and expression of apoptosis markers (Bcl-2, Caspase-3), water channel protein aquaporin-3, and tight junction proteins (zonula occludens-1, occludin) were subsequently evaluated. Results: Despite preserved conventional renal function, IVIM MRI detected early renal alterations 24 h after cardiac arrest. Compared with the sham group, eADC, D, ADC, D*, and f values were all significantly reduced. Histopathological analysis revealed pronounced tubular epithelial injury, increased apoptosis (Bcl-2, p < 0.001; Caspase-3, p = 0.010), and reduced expression of aquaporin-3 ( p = 0.039), zonula occludens-1 ( p < 0.001), and occludin ( p < 0.001). Conclusion: IVIM MRI enables early, noninvasive detection of renal injury 24 h after cardiac arrest and CPR, reflecting tubular injury, tight junction disruption, and perfusion deficits, potentially facilitating timely intervention and improving prognosis. Relevance statement: This approach may facilitate individualized therapy and improve post-cardiac arrest syndrome outcome. Key Points: The long-term outcomes of cardiac arrest remain poor due to post-cardiac arrest syndrome. IVIM MRI enables early, noninvasive detection of renal injury after cardiac arrest and cardiopulmonary resuscitation. This approach may facilitate individualized therapy and improve post-cardiac arrest syndrome. Graphical

Background: The role of ferroptosis in Cardiac arrest (CA)‐induced cerebral edema remains unclear. Purpose: To investigate whether ferroptosis contributes to blood–brain barrier (BBB) disruption and aquaporin‐4 (AQP4) dysfunction following CA. Study Type: Prospective. Animal Model: Asphyxia‐induced CA rat model. Forty two rats were used and assigned to the CA (24) and the sham (18) group. Field Strength/Sequence: T2‐weighted anatomical imaging with 2D turbo spin‐echo sequence, QSM with 3D GRE sequence, IVIM with 2D RESOLVE EPI sequence, ¹ H‐MRS with SVS‐PRESS sequence. Assessment: Multiparametric MRI was performed 24 h after return of spontaneous circulation (ROSC). Imaging findings were validated using histology, immunohistochemistry, Western blot, and transmission electron microscopy. Statistical Tests: Unpaired two‐tailed Student's T ‐test was used. A p value less than 0.05 was considered statistically significant. Results: CA led to marked neurological deficits, although no obvious abnormalities were observed on T ₂ ‐weighted MRI. QSM revealed marked iron accumulation in the hippocampus, which was associated with elevated ROS, lipid peroxidation, and altered expression of ferroptosis‐related markers (downregulation of GPX4 and upregulation of ACSL4). Electron microscopy confirmed mitochondrial changes characteristic of ferroptosis. Ferroptosis‐induced lipid peroxidation resulted in degradation of tight‐junction proteins (ZO‐1 and occludin), BBB leakage (elevated serum S100β), and loss of AQP4 polarity. IVIM showed a selective reduction in true tissue diffusivity (IVIM D ), indicating cytotoxic edema. ¹ H‐MRS revealed decreased N ‐acetylaspartate, increased lipid peaks, and reduced myo‐inositol levels, consistent with neuronal death and astrocytic swelling. Data Conclusion: This study provides evidence for a sequential cascade after CA and ROSC in which iron overload is associated with ferroptosis, which is linked to disruption of the blood–brain barrier. This barrier disruption coincides with AQP4 depolarization, inducing cytotoxic and vasogenic edema, which, along with depolarization, is accompanied by neuronal death. Multimodal MRI noninvasively captures this process, offering an early detection and monitoring platform for ferroptosis‐related brain injury and underscoring its potential as a translational tool for neuroprotective interventions. Evidence Level: 1. Technical Efficacy: Stage 1.

Objective: This study aims to construct a nomogram using habitat radiomics, radiomics, and clinical features derived from multiparametric MRI (mpMRI) to improve the accuracy of preoperative prediction of lymphovascular invasion (LVI) status in rectal cancer patients. Methods: Data from 372 pathologically confirmed rectal cancer cases were retrospectively collected from two centers. Data from Center 1 were randomly split in a 7:3 ratio into a training cohort ( n = 201) and an internal validation cohort ( n = 87). Data from Center 2 served as external validation ( n = 84). K-means clustering was used to divide the tumor into three subregions. Radiomics features were extracted from regions of interest and selected, and three machine learning algorithms were employed to construct radiomics models. A nomogram was created by integrating clinical, radiomics, and habitat radiomics features. The model’s predictive accuracy was assessed using AUC metrics, while practical clinical applicability was evaluated via calibration plots and decision curve analysis. SHAP values were employed to measure the contribution of individual radiomic features to predictive outcomes, thereby offering transparent insights for clinical decision-making processes. Results: The nomogram model outperformed other single models in predicting LVI, with AUCs of 0.978, 0.909, and 0.889 in the training, validation, and external test sets, respectively. Compared with the intratumoral model alone, the nomogram model achieved improvements of 20.3%, 14.1%, and 13.8%, respectively. Conclusion: The nomogram model developed in this study significantly improved the accuracy of predicting preoperative lymphovascular invasion status in rectal cancer.

As stroke is a leading global cause of disability and death, and acute ischemic stroke (AIS) constitutes ~ 80% of strokes, prognostic assessmen

Purpose: This study aimed to evaluate the influence of the triglyceride-glucose index (TyG index) on clinical outcomes and to develop nomogram models for predicting early neurological deterioration (END) and long-term prognosis in acute ischemic stroke (AIS) patients following intravenous thrombolytic (IVT) therapy. Methods: We conducted a multi-center retrospective cohort study involving 333 AIS patients treated with IVT. The short-term and long-term outcomes were defined as the occurrence of END and 90-day prognosis. Multivariate logistic regression was used to develop nomogram models for forecasting these clinical outcomes. Results: Patients in the high-TyG group exhibited significantly higher risks of END ( P = 0.0010) and poor 90-day outcomes ( P = 0.0012). Independent risk factors for END included a lower baseline NIHSS score, delayed door-to-needle time (DNT), reduced ASPECTS score, elevated TyG index, higher potassium (K+) levels, and incomplete Willis artery. Additionally, a higher initial NIHSS, increased TyG levels, presence of END, and a history of hypertension were predictors of poor prognosis. Based on the identified risk factors, two nomogram models yielded AUC values of 0.746 and 0.849 for predicting END and poor prognosis, respectively. NIHSS scores, TyG index, and admission glucose levels (Glu) emerged as prognostic indicators across all patients, while higher mean platelet volume (MPV) and history of stroke were identified as novel risk factors for poor prognosis in NO-END group. Conclusion: A higher TyG index correlates with poor clinical outcomes in AIS patients post-IVT. The nomograms combining the TyG index with various factors enhanced risk prediction for END and poor prognosis.

BACKGROUND AND OBJECTIVES: The incidence of imminent new vertebral fracture (NVF) is notably high after vertebral augmentation (VA), but accurately assessing the imminent risk of NVF remains a great challenge. The aim of this study was to investigate whether the MRI-based vertebral bone quality (VBQ) score can predict the risk of imminent NVF after VA within a 2-year period. METHODS: A total of 135 patients age 50 years and older who suffered from painful osteoporotic vertebral compression fracture and treated with VA were enrolled in this retrospective study. Each patient's VBQ scores were calculated from T1-weighted, T2-weighted, and short tau inversion recovery sequences of preoperative lumbar MRI. The clinical factors and VBQ score were integrated to create a predictive model by using the logistic regression algorithm and visualize by nomogram. Receiver operating characteristic curve, calibration curve, and decision curve analyses were used to evaluate the predictive performance of the nomogram. RESULTS: The mean VBQ-T1WI and VBQ-T2WI scores of the NVF group were 4.61 ± 0.55 and 0.89 ± 0.14, respectively, which were significantly higher than those of the without NVF group (3.99 ± 0.54 and 0.79 ± 0.12, respectively, P < .001), as well as the VBQ-combined score (0.75 ± 1.30 vs −0.80 ± 1.26, P < .001), which is the combination of VBQ-T1WI and VBQ-T2WI scores. On multivariate analysis, the predictors of imminent NVF included age (odds ratio [OR] = 1.064, 95% CI = 1.009-1.122, P = .022), previous vertebral fracture (OR = 2.089, 95% CI = 0.888-4.915, P = .091), and VBQ-combined score (OR = 2.239, 95% CI = 1.529-3.279, P < .001). The nomogram achieved superior performance with an area under the receiver operating characteristic curve of 0.838 (95% CI: 0.773-0.904) in predicting the imminent NVF compared to the clinical factors or VBQ-combined score alone. CONCLUSION: The VBQ score obtained from lumbar MRI can be used to assess the VBQ and predict the imminent NVF after VA in patients with osteoporotic vertebral compression fracture.

Background Imminent new vertebral fracture (NVF) is highly prevalent after vertebral augmentation (VA). An accurate assessment of the imminent risk of NVF could help to develop prompt treatment strategies. Purpose To develop and validate predictive models that integrated the radiomic features and clinical risk factors based on machine learning algorithms to evaluate the imminent risk of NVF. Materials and methods In this retrospective study, a total of 168 patients with painful osteoporotic vertebral compression fractures treated with VA were evaluated. Radiomic features of L1 vertebrae based on lumbar T2-weighted images were obtained. Univariate and LASSO-regression analyses were applied to select the optimal features and construct radiomic signature. The radiomic signature and clinical signature were integrated to develop a predictive model by using machine learning algorithms including LR, RF, SVM, and XGBoost. Receiver operating characteristic curve and calibration curve analyses were used to evaluate the predictive performance of the models. Results The radiomic-XGBoost model with the highest AUC of 0.93 of the training cohort and 0.9 of the test cohort among the machine learning algorithms. The combined-XGBoost model with the best performance with an AUC of 0.9 in the training cohort and 0.9 in the test cohort. The radiomic-XGBoost model and combined-XGBoost model achieved better performance to assess the imminent risk of NVF than that of the clinical risk factors alone (p < 0.05). Conclusion Radiomic and machine learning modeling based on T2W images of preoperative lumbar MRI had an excellent ability to evaluate the imminent risk of NVF after VA.

Background: Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation. Methods: A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models. Results: The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model. Conclusions: The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.