Utility of Advanced DWI in the Detection of Spinal Cord Microstructural Alterations and Assessment of Neurologic Function in Cervical Spondylotic Myelopathy Patients.

BACKGROUND: Diffusion-weighted imaging (DWI) can quantify the microstructural changes in the spinal cord. It might be a substitute for T2 increased signal intensity (ISI) for cervical spondylotic myelopathy (CSM) evaluation and prognosis. PURPOSE: The purpose of the study is to investigate the relationship between DWI metrics and neurologic function of patients with CSM. STUDY TYPE: Retrospective. POPULATION: Forty-eight patients with CSM (18.8% females) and 36 healthy controls (HCs, 25.0% females). FIELD STRENGTH/SEQUENCE: 3 T; spin-echo echo-planar imaging-DWI; turbo spin-echo T1/T2; multi-echo gradient echo T2*. ASSESSMENT: For patients, conventional MRI indicators (presence and grades of T2 ISI), DWI indicators (neurite orientation dispersion and density imaging [NODDI]-derived isotropic volume fraction [ISOVF], intracellular volume fraction, and orientation dispersion index [ODI], diffusion tensor imaging [DTI]-derived fractional anisotropy [FA] and mean diffusivity [MD], and diffusion kurtosis imaging [DKI]-derived FA, MD, and mean kurtosis), clinical conditions, and modified Japanese Orthopaedic Association (mJOA) were recorded before the surgery. Neurologic function improvement was measured by the 3-month follow-up recovery rate (RR). For HCs, DWI, and mJOA were measured as baseline comparison. STATISTICAL TESTS: Continuous (categorical) variables were compared between patients and HCs using Student's t-tests or Mann-Whitney U tests (chi-square or Fisher exact tests). The relationships between DWI metrics/conventional MRI findings, and the pre-operative mJOA/RR were assessed using correlation and multivariate analysis. P < 0.05 was considered statistically significant. RESULTS: Among patients, grades of T2 ISI were not correlated with pre-surgical mJOA/RR (P = 0.717  and 0.175, respectively). NODDI ODI correlated with pre-operative mJOA (r = -0.31). DTI FA, DKI FA, and NODDI ISOVF were correlated with the recovery rate (r = 0.31, 0.41, and -0.34, respectively). In multivariate analysis, NODDI ODI (DTI FA, DKI FA, NODDI ISOVF) significantly contributed to the pre-operative mJOA (RR) after adjusting for age. DATA CONCLUSION: DTI FA, DKI FA, and NODDI ISOVF are predictors for prognosis in patients with CSM. NODDI ODI can be used to evaluate CSM severity. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 5.

Predicting postoperative recovery in cervical spondylotic myelopathy: construction and interpretation of T2*-weighted radiomic-based extra trees models.

OBJECTIVES: Conventional MRI may not be ideal for predicting cervical spondylotic myelopathy (CSM) prognosis. In this study, we used radiomics in predicting postoperative recovery in CSM. We aimed to develop and validate radiomic feature-based extra trees models. METHODS: There were 151 patients with CSM who underwent preoperative T2-/ T2*-weighted imaging (WI) and surgery. They were divided into good/poor outcome groups based on the recovery rate. Datasets from multiple scanners were randomised into training and internal validation sets, while the dataset from an independent scanner was used for external validation. Radiomic features were extracted from the transverse spinal cord at the maximum compressed level. Threshold selection algorithm, collinearity removal, and tree-based feature selection were applied sequentially in the training set to obtain the optimal radiomic features. The classification of intramedullary increased signal on T2/T2*WI and compression ratio of the spinal cord on T2*WI were selected as the conventional MRI features. Clinical features were age, preoperative mJOA, and symptom duration. Four models were constructed: radiological, radiomic, clinical-radiological, and clinical-radiomic. An AUC significantly > 0.5 was considered meaningful predictive performance based on the DeLong test. The mean decrease in impurity was used to measure feature importance. p < 0.05 was considered statistically significant. RESULTS: On internal and external validations, AUCs of the radiomic and clinical-radiomic models, and radiological and clinical-radiological models ranged from 0.71 to 0.81 (significantly > 0.5) and 0.40 to 0.55, respectively. Wavelet-LL first-order variance was the most important feature in the radiomic model. CONCLUSION: Radiomic features, especially wavelet-LL first-order variance, contribute to meaningful predictive models for CSM prognosis. KEY POINTS: • Conventional MRI features may not be ideal in predicting prognosis. • Radiomics provides greater predictive efficiency in the recovery from cervical spondylotic myelopathy.

Expression of urotensin II is positively correlated with pyroptosis-related molecules in patients with severe preeclampsia.

Purpose: We studied the expression of urotensin II (UII) and its relationships with markers of pyroptosis in preeclampsia. Methods: 48 pregnant subjects were recruited consisting of 28 severe preeclampsia pregnancies (SPE) and 20 healthy pregnancies. We detected expressions of UII and markers of pyroptosis such as NLR-family pyrin domain (PYD)-containing 3 (NLRP-3), caspase-1/4/5, interleukin-1ß (IL-1ß), and gasdermin D (GSDMD) in placentas of patients with SPE and healthy pregnancies. Results: SPE group have higher expression of UII and NLRP-3, caspase-1, interleukin-1ß (IL-1ß), and GSDMD than that normal controls by IHC, real-time PCR, and western blot. IHC analysis manifests that the expressions of UII and pyroptosis-related molecules are mainly located in the placental cytotrophoblasts. Expressions of UII mRNA and protein are significantly positively correlated with pyroptosis marker such as NLRP3, caspase-1, GSDMD mRNA and protein by Pearson correlation analysis. Moreover, UII, NLRP-3, caspase-1, interleukin-1ß (IL-1ß), and GSDMD are positively related with systolic blood pressure, meanwhile caspase-1 and GSDMD are positively correlated with urine protein in SPE patients. We firstly verify that UII has a positive correlation with pyroptosis markers in placentas of preeclampsia patients; besides, pyroptosis-related proteins are positively correlated with systolic blood pressure and urine protein in patients with severe preeclampsia.

Tracking Neural Stem Cells in vivo: Achievements and Limitations.

Neural stem cell (NSC) therapies are developing rapidly and have been proposed as a treatment option for various neurological diseases, such as stroke, Parkinson's disease and multiple sclerosis. However, monitoring transplanted NSCs, exploring their location and migration, and evaluating their efficacy and safety have all become serious and important issues. Two main problems in tracking NSCs have been noted: labeling them for visibility and imaging them. Direct labeling and reporter gene labeling are the two main methods for labeling stem cells. Magnetic resonance imaging and nuclear imaging, including positron emission tomography, single-photon emission computed tomography, and optical imaging, are the most commonly used imaging techniques. Each has its strengths and weaknesses. Thus, multimodal imaging, which combines two or more imaging methods to complement the advantages and disadvantages of each, has garnered increased attention. Advances in image fusion and nanotechnology, as well as the exploration of new tracers and new imaging modalities have substantially facilitated the development of NSC tracking technology. However, the safety issues related to tracking and long-term tracking of cell viability are still challenges. In this review, we discuss the merits and defects of different labeling and imaging methods, as well as recent advances, challenges and prospects in NSC tracking.

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T2*-weighted images of cervical spondylotic myelopathy.

INTRODUCTION: Predicting the postoperative neurological function of cervical spondylotic myelopathy (CSM) patients is generally based on conventional magnetic resonance imaging (MRI) patterns, but this approach is not completely satisfactory. This study utilized radiomics, which produced advanced objective and quantitative indicators, and machine learning to develop, validate, test, and compare models for predicting the postoperative prognosis of CSM. MATERIALS AND METHODS: In total, 151 CSM patients undergoing surgical treatment and preoperative MRI was retrospectively collected and divided into good/poor outcome groups based on postoperative modified Japanese Orthopedic Association (mJOA) scores. The datasets obtained from several scanners (an independent  scanner) for the training (testing) cohort were used for cross-validation (CV). Radiological models based on the intramedullary hyperintensity and compression ratio were constructed with 14 binary classifiers. Radiomic models based on 237 robust radiomic features were constructed with the same 14 binary classifiers in combination with 7 feature reduction methods, resulting in 98 models. The main outcome measures were the area under the receiver operating characteristic curve (AUROC) and accuracy. RESULTS: Forty-one (11) radiomic models were superior to random guessing during CV (testing), with significant increased AUROC and/or accuracy (P AUROC < .05 and/or P accuracy < .05). One radiological model performed better than random guessing during CV (P accuracy < .05). In the testing cohort, the linear SVM preprocessor + SVM, the best radiomic model (AUROC: 0.74 ± 0.08, accuracy: 0.73 ± 0.07), overperformed the best radiological model (P AUROC = .048). CONCLUSION: Radiomic features can predict postoperative spinal cord function in CSM patients. The linear SVM preprocessor + SVM has great application potential in building radiomic models.