MRI-based radiomics assessment of the imminent new vertebral fracture after vertebral augmentation.

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.

Development and validation of a machine learning model to predict imminent new vertebral fractures after vertebral augmentation.

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.

Assessment of sepsis-associated encephalopathy by quantitative magnetic resonance spectroscopy in a rat model of cecal ligation and puncture.

Proton magnetic resonance spectroscopy (1H-MRS) is the only non-invasive technique to quantify neurometabolic compounds in the living brain. We used 1H-MRS to evaluate the brain metabolites in a rat model of Sepsis-associated encephalopathy (SAE) established by cecal ligation and puncture (CLP). 36 male Sprague-Dawley rats were randomly divided into sham and CLP groups. Each group was further divided into three subgroups: subgroup O, subgroup M, and subgroup N. Neurological function assessments were performed on the animals in the subgroup O and subgroup N at 24 h, 48 h, and 72 h. The animals in the subgroup M were examined by magnetic resonance imaging (MRI) at 12 h after CLP. Compared with the sham group, the ratio of N-acetylaspartate (NAA) to creatine (Cr) in the hippocampus was significantly lower in the CLP group. The respective ratios of lactate (Lac), myo-inositol (mIns), glutamate and glutamine (Glx), lipid (Lip), and choline (Cho) to Cr in the CLP group were clearly higher than those in the sham group. Cytochrome c, intimately related to oxidative stress, was elevated in the CLP group. Neurofilament light (NfL) chain and glial fibrillary acidic protein (GFAP) scores in the CLP group were significantly higher than those in the sham group, while zonula occludens-1 (ZO-1) was downregulated. Compared with the sham group, the CLP group displayed higher values of oxygen extraction fraction (OEF), central venous-arterial partial pressure of carbon dioxide (P (cv-a) CO2), and central venous lactate (VLac). In contrast, jugular venous oxygen saturation (SjvO2) declined. In the present study, 1H-MRS could be used to quantitatively assess brain injury in terms of microcirculation disorder, oxidative stress, blood-brain barrier disruption, and glial cell activation through changes in metabolites within brain tissue.

MRI-Based Vertebral Bone Quality Score Can Predict the Imminent New Vertebral Fracture After Vertebral Augmentation.

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.

A Comparative Study of a Nomogram and Machine Learning Models in Predicting Early Hematoma Expansion in Hypertensive Intracerebral Hemorrhage.

RATIONALE AND OBJECTIVES: Early identification for hematoma expansion can help improve patient outcomes. Presently, there are many methods to predict hematoma expansion. This study compared a variety of models to find a model suitable for clinical promotion. MATERIALS AND METHODS: Non-contrast head CT images and clinical data were collected from 203 patients diagnosed with hypertensive intracerebral hemorrhage. Radiomics features were extracted from all CT images, and the dataset was randomly divided into training and validation sets (7:3 ratio) after applying the synthetic minority oversampling method. The radiomics score (Radscore) was calculated using least absolute shrinkage and selection operator (LASSO) regression, combined with selected clinical predictors, to develop a nomogram and four machine learning (ML) models: logistic regression, random forest, support vector machine, and extreme gradient boosting (XGBoost). Discrimination, calibration and clinical usefulness of the nomogram and ML models were assessed. RESULTS: The nomogram and ML models were integrated with Radscore and clinical predictors. The nomogram demonstrated favorable discriminatory ability in the training set with an AUC of 0.80, which was confirmed in the validation set (AUC=0.76). Among the ML models, the XGBoost model achieved the highest AUC (training set=0.89 and validation set=0.85), surpassing that of the nomogram. The XGBoost model exhibited good clinical usefulness. CONCLUSION: Both the nomogram and ML models constructed by non-contrast head CT image-based Radscore integrated with clinical predictors can predict early hematoma expansion of hypertensive intracerebral hemorrhage, and the XGBoost model had the highest prediction performance and best clinical usefulness.

Preoperative Prediction of New Vertebral Fractures after Vertebral Augmentation with a Radiomics Nomogram.

The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.

Study on Umbilical Cord-Matrix Stem Cells Transplantation for Treatment of Acute Traumatic Brain Injury in Rats.

AIM: To evaluate the effect of umbilical cord derived mesenchymal stem cells (UC-MSCs) transplantation on traumatic brain injury (TBI). MATERIAL AND METHODS: UC-MSCs were isolated from human umbilical cord and TBI rat model was constructed. 30 male SD rats were randomly divided into 3 groups: control group, TBI group and MSCs transplantation group. Rats in MSCs group received the injection of a total of 1.5 C- 106 MSCs (25 I»l) via ventricle at operated ventricular coordinates (0 at bregma, 1.5 mm at lateral, 1.1 mm at behind, 4.5 mm in depth). RESULTS: 80% confluence of cells was formed from tissue at day 10 and the amount of CD90, CD73, CD105 positive cells increased correspondingly. In TBI model, clear hyperemia, edema and obvious infiltration of inflammatory cells in brain tissue were found. However, the manifestations were alleviated after the treatment of MSCs. In MSCs group, GFP in the brain tissue and the area around the vessels were found after the injection, while the expression levels of micro-vessel density (MVD), brain-derived neurotrophic factor (BDNF) and glial fibrillary acidic protein (GFAP) were elevated. CONCLUSION: UC-MSCs transplantation for treatment of acute TBI could effectively reduce the injury and improve the vascular reconstruction.