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.

Predicting postoperative recovery in cervical spondylotic myelopathy: construction and interpretation of T-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.

Optimal machine learning methods for radiomic prediction models: Clinical application for preoperative T*-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.

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.

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.