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.

Inhibition of the STAT3 signaling pathway is involved in the antitumor activity of cepharanthine in SaOS2 cells.

AIM: To investigate the molecular mechanisms underlying the antitumor activity of cepharanthine (CEP), an alkaloid extracted from Stephania cepharantha Hayata. METHODS: Human osteosarcoma cell line SaOS2 was used. MTT assay, Hoechst 33342 nuclear staining, flow cytometry, Western blotting and nude mouse xenografts of SaOS2 cells were applied to examine the antitumor activity of CEP in vitro and in vivo. The expression levels of STAT3 and its downstream signaling molecules were measured with Western blotting and immunochemistry analysis. The activity of STAT3 was detected based on the phosphorylation level of STAT3, luciferase gene reporter assay and translocation of STAT3 to the nucleus. RESULTS: Treatment of SaOS2 cells with CEP (2.5-20 µmol/L) inhibited the cell growth in a concentration- and time-dependent manner. CEP (10 µmol/L) caused cell cycle arrest at G(1) phase and induced apoptosis of SaOS2 cells. CEP (10 and 15 µmol/L) significantly decreased the expression of STAT3 in SaOS2 cells. Furthermore, CEP (5 and 10 µmol/L) significantly inhibited the expression of target genes of STAT3, including the anti-apoptotic gene Bcl-xL and the cell cycle regulators c-Myc and cyclin D1. In nude mouse xenografts of SaOS2 cells, CEP (20 mg·kg(-1)·d(-1), ip for 19 d) significantly reduced the volume and weight of the tumor. CONCLUSION: Our findings suggest that inhibition of STAT3 signaling pathway is involved in the anti-tumor activity of CEP.

BgNet: Classification of benign and malignant tumors with MRI multi-plane attention learning.

Objectives: To propose a deep learning-based classification framework, which can carry out patient-level benign and malignant tumors classification according to the patient's multi-plane images and clinical information. Methods: A total of 430 cases of spinal tumor, including axial and sagittal plane images by MRI, of which 297 cases for training (14072 images), and 133 cases for testing (6161 images) were included. Based on the bipartite graph and attention learning, this study proposed a multi-plane attention learning framework, BgNet, for benign and malignant tumor diagnosis. In a bipartite graph structure, the tumor area in each plane is used as the vertex of the graph, and the matching between different planes is used as the edge of the graph. The tumor areas from different plane images are spliced at the input layer. And based on the convolutional neural network ResNet and visual attention learning model Swin-Transformer, this study proposed a feature fusion model named ResNetST for combining both global and local information to extract the correlation features of multiple planes. The proposed BgNet consists of five modules including a multi-plane fusion module based on the bipartite graph, input layer fusion module, feature layer fusion module, decision layer fusion module, and output module. These modules are respectively used for multi-level fusion of patient multi-plane image data to realize the comprehensive diagnosis of benign and malignant tumors at the patient level. Results: The accuracy (ACC: 79.7%) of the proposed BgNet with multi-plane was higher than that with a single plane, and higher than or equal to the four doctors' ACC (D1: 70.7%, p=0.219; D2: 54.1%, p<0.005; D3: 79.7%, p=0.006; D4: 72.9%, p=0.178). Moreover, the diagnostic accuracy and speed of doctors can be further improved with the aid of BgNet, the ACC of D1, D2, D3, and D4 improved by 4.5%, 21.8%, 0.8%, and 3.8%, respectively. Conclusions: The proposed deep learning framework BgNet can classify benign and malignant tumors effectively, and can help doctors improve their diagnostic efficiency and accuracy. The code is available at https://github.com/research-med/BgNet.

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.