Deep learning model for the automated detection and classification of central canal and neural foraminal stenosis upon cervical spine magnetic resonance imaging.

Background: A deep learning (DL) model that can automatically detect and classify cervical canal and neural foraminal stenosis using cervical spine magnetic resonance imaging (MRI) can improve diagnostic accuracy and efficiency.

Methods: A method comprising region-of-interest (ROI) detection and cascade prediction was formulated for diagnosing cervical spinal stenosis based on a DL model. First, three part-specific convolutional neural networks were employed to detect the ROIs in different parts of the cervical MR images.

Assessment of axial spondyloarthritis activity using a magnetic resonance imaging-based multi-region-of-interest fusion model.

Background: Identifying axial spondyloarthritis (axSpA) activity early and accurately is essential for treating physicians to adjust treatment plans and guide clinical decisions promptly. The current literature is mostly focused on axSpA diagnosis, and there has been thus far, no study that reported the use of a radiomics approach for differentiating axSpA disease activity. In this study, the aim was to develop a radiomics model for differentiating active from non-active axSpA based on fat-suppressed (FS) T2-weighted (T2w) magnetic resonance imaging (MRI) of sacroiliac joints.

Radiomics from Mesorectal Blood Vessels and Lymph Nodes: A Novel Prognostic Predictor for Rectal Cancer with Neoadjuvant Therapy.

The objective of our study is to investigate the predictive value of various combinations of radiomic features from intratumoral and different peritumoral regions of interest (ROIs) for achieving a good pathological response (pGR) following neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). This retrospective study was conducted using data from LARC patients who underwent nCRT between 2013 and 2021. Patients were divided into training and validation cohorts at a ratio of 4:1.

Prediction of Primary Tumor Sites in Spinal Metastases Using a ResNet-50 Convolutional Neural Network Based on MRI.

We aim to investigate the feasibility and evaluate the performance of a ResNet-50 convolutional neural network (CNN) based on magnetic resonance imaging (MRI) in predicting primary tumor sites in spinal metastases. Conventional sequences (T1-weighted, T2-weighted, and fat-suppressed T2-weighted sequences) MRIs of spinal metastases patients confirmed by pathology from August 2006 to August 2019 were retrospectively analyzed. Patients were partitioned into non-overlapping sets of 90% for training and 10% for testing.

Characteristics and outcomes of patients with primary abdominopelvic aggressive angiomyxoma: a retrospective review of 12 consecutive cases from a sarcoma referral center.

Background: Aggressive angiomyxoma (AAM) is a rare mesenchymal tumor that mostly arises from the pelvic and perineal soft tissues. Few studies reported its characteristics and outcomes previously due to its rarity and challenges of treatments. This study aimed to investigate the clinical characteristics as well as surgical and short-term survival outcomes of primary abdominopelvic AAM.

Primary leiomyosarcoma of the spine: an analysis of imaging manifestations and clinicopathological findings.

Background: Primary leiomyosarcoma of the spine is extremely rare and lacks specific clinical symptoms. This study investigated the imaging manifestations and clinicopathological findings of primary leiomyosarcoma of the spine, aiming to improve the radiologists' understanding of the disease and reduce misdiagnoses.

Methods: The clinical, imaging, and pathological manifestations in eleven patients with pathologically confirmed primary leiomyosarcoma of the spine were retrospectively analyzed.

Differentiation of predominantly osteolytic from osteoblastic spinal metastases based on standard magnetic resonance imaging sequences: a comparison of radiomics model versus semantic features logistic regression model findings.

Background: The aim of this study was to compare the ability of a standard magnetic resonance imaging (MRI)-based radiomics model and a semantic features logistic regression model in differentiating between predominantly osteolytic and osteoblastic spinal metastases.

Methods: We retrospectively analyzed standard MRIs and computed tomography (CT) images of 78 lesions of spinal metastases, of which 52 and 26 were predominantly osteolytic and osteoblastic, respectively. CT images were used as references for determining the sensitivity and specificity of standard MRI.

Intravoxel incoherent motion to differentiate spinal metastasis: A pilot study.

Background: To investigate the value of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to discriminate spinal metastasis from tuberculous spondylitis.

Methods: This study included 50 patients with spinal metastasis (32 lung cancer, 7 breast cancer, 11 renal cancer), and 20 with tuberculous spondylitis. The IVIM parameters, including the single-index model (apparent diffusion coefficient (ADC)-stand), double exponential model (ADC, ADC, and f), and the stretched-exponential model parameters (distributed diffusion coefficient (DDC) and α), were acquired.

Case report: Castleman's disease involving the renal sinus resembling renal cell carcinoma.

Introduction: Castleman's disease (CD) is a rare benign lymphoproliferative disease that frequently involves the mediastinal thorax and the neck lymph nodes. It rarely affects extrathoracic presentations, with even fewer presentations in the renal sinus.

Patient Concerns: In this report, we present a case of a 40-year-old woman with no significant past medical history who presented Castleman's disease arising in the renal sinus.

A Multiparametric Method Based on Clinical and CT-Based Radiomics to Predict the Expression of p53 and VEGF in Patients With Spinal Giant Cell Tumor of Bone.

Purpose: This project aimed to assess the significance of vascular endothelial growth factor (VEGF) and p53 for predicting progression-free survival (PFS) in patients with spinal giant cell tumor of bone (GCTB) and to construct models for predicting these two biomarkers based on clinical and computer tomography (CT) radiomics to identify high-risk patients for improving treatment.

Material And Methods: A retrospective study was performed from April 2009 to January 2019. A total of 80 patients with spinal GCTB who underwent surgery in our institution were identified.

Epithelioid hemangioendothelioma of the spine: an analysis of imaging findings.

Background: Epithelioid hemangioendothelioma (EHE) is a low-grade malignant vascular neoplasm with the potential to metastasize. Primary EHE of the spine is very rare and an accurate diagnosis is crucial to treatment planning. We aim to investigate the imaging and clinical data of spinal EHE to improve the understanding of the disease.

Clinical Significance of Preoperative CT and MR Imaging Findings in the Prediction of Postoperative Recurrence of Spinal Giant Cell Tumor of Bone.

Objectives: To explore the predictive value of preoperative imaging in patients with spinal giant cell tumor of bone (GCTB) for postoperative recurrence and risk stratification.

Methods: Clinical data for 62 cases of spinal GCTB diagnosed and treated at our hospital from 2008 to 2018 were identified. All patients were followed up for more than 2 years according to the clinical guidelines after surgery.

[Advances in Radiomics of Cerebral Metastases].

Cerebral metastases are the most common intracranial tumors in adults,with an increasing incidence in recent years.Radiomics can quantitatively analyze and process medical images to guide clinical practice.In recent years,CT and MRI-based radiomics has been gradually applied to the precise diagnosis and treatment of cerebral metastases,such as the precise detection and segmentation of tumors,the differential diagnosis with other cerebral tumors,the identification of primary tumors,the evaluation of treatment efficacy,and the prediction of prognosis.

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning.

Objectives: To evaluate the performance of deep learning using ResNet50 in differentiation of benign and malignant vertebral fracture on CT.

Methods: A dataset of 433 patients confirmed with 296 malignant and 137 benign fractures was retrospectively selected from our spinal CT image database. A senior radiologist performed visual reading to evaluate six imaging features, and three junior radiologists gave diagnostic prediction.

Prediction of the early recurrence in spinal giant cell tumor of bone using radiomics of preoperative CT: Long-term outcome of 62 consecutive patients.

Objectives: To determine if radiomics analysis based on preoperative computed tomography (CT) can predict early postoperative recurrence of giant cell tumor of bone (GCTB) in the spine.

Methods: In a retrospective review, 62 patients with pathologically confirmed spinal GCTB from March 2008 to February 2018, with a minimum follow-up of 24 months, were identified. The mean follow-up was 73.

Early response assessment after CyberKnife stereotactic radiosurgery for symptomatic vertebral hemangioma by quantitative parameters from dynamic contrast-enhanced MRI.

Purpose: The present study aimed to explore the value of DCE-MRI to evaluate the early efficacy of CyberKnife stereotactic radiosurgery in patients with symptomatic vertebral hemangioma (SVH).

Methods: A retrospective analysis of patients with spinal SVH who underwent CyberKnife stereotactic radiosurgery from January 2017 to August 2019 was performed. All patients underwent DCE-MRI before treatment and three months after treatment.

Cerebral carbon dioxide embolism after kidney cancer laparoscopic surgery with full neurological recovery: A case report.

Rationale: Cerebral carbon dioxide embolism (CCDE) is a rare cause of stroke and is a recognized life-threatening complication.CCDE may result from direct intravascular CO2 insufflation during surgery. Due to the lack of typical clinical manifestations, the disease is often missed or mistaken for another condition.

[Applications of Artificial Intelligence in Musculoskeletal System Imaging].

Artificial intelligence (AI) represents the latest wave of computer revolution and is considered revolutionary technology in many industries including healthcare. AI has been applied in medical imaging mainly due to the improvement of computational learning,big data mining,and innovations of neural network architecture. AI can improve the efficiency and accuracy of imaging diagnosis and reduce medical cost;also,it can be used to predict the disease risk.

Use of monoexponential diffusion-weighted imaging and diffusion kurtosis imaging and dynamic contrast-enhanced-MRI for the differentiation of spinal tumors.

Purpose: To explore the diagnostic value of monoexponential diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and dynamic contrast-enhanced (DCE)-MRI for differentiating between spinal malignant and non-malignant tumors lacking typical imaging signs and correlation between the parameters of the three models.

Methods: DWI, DKI, and DCE-MRI examinations were performed in 39 and 27 cases of spinal malignant and non-malignant tumors, respectively. Two radiologists independently evaluated apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) of the DWI and DKI models, and volume transfer constant (K), rate constant (k), and extracellular extravascular volume ratio (v) of the DCE-MRI model for post-processing analyses.

Diagnosis of spinal lesions using perfusion parameters measured by DCE-MRI and metabolism parameters measured by PET/CT.

Purpose: To investigate the correlation of parameters measured by dynamic-contrast-enhanced MRI (DCE-MRI) and F-FDG PET/CT in spinal tumors, and their role in differential diagnosis.

Methods: A total of 49 patients with pathologically confirmed spinal tumors, including 38 malignant, six benign and five borderline tumors, were analyzed. The MRI and PET/CT were done within 3 days, before biopsy.

Enhancing Prostate-Cancer-Specific MRI by Genetic Amplified Nanoparticle Tumor Homing.

Precise localization and visualization of early-stage prostate cancer (PCa) is critical to improve the success of focal ablation and reduce cancer mortality. However, it remains challenging under the current imaging techniques due to the heterogeneous nature of PCa and the suboptimal sensitivity of the techniques themselves. Herein, a novel genetic amplified nanoparticle tumor-homing strategy to enhance the MRI accuracy of ultrasmall PCa lesions is reported.

Differentiation of spinal metastases originated from lung and other cancers using radiomics and deep learning based on DCE-MRI.

Purpose: To differentiate metastatic lesions in the spine originated from primary lung cancer and other cancers using radiomics and deep learning, compared to traditional hot-spot ROI analysis.

Methods: In a retrospective review of clinical spinal MRI database with a dynamic contrast enhanced (DCE) sequence, a total of 61 patients without prior cancer diagnosis and later confirmed to have metastases (30 lung; 31 non-lung cancers) were identified. For hot-spot analysis, a manual ROI was placed to calculate three heuristic parameters from the wash-in, maximum, and wash-out phases in the DCE kinetics.

Spinal cellular schwannoma: An analysis of imaging manifestation and clinicopathological findings.

Objectives: Cellular schwannoma that occurs in the spine is relatively rare. Herein, we retrospectively analyzed the clinical and imaging data from nine cases of spinal cellular schwannoma.

Materials And Methods: The clinical, imaging data and pathological manifestations were retrospectively analyzed from nine cases of pathologically confirmed spinal cellular schwannoma.

Solitary fibrous tumour of the spine: imaging features of a commonly misdiagnosed entity.

Objective: Solitary fibrous tumours (SFTs) occurring in the spine are rare. Herein, we review the clinical and imaging data of spinal SFT.

Methods: We retrospectively analysed eight cases of pathologically confirmed spinal SFT imaging and clinical data, pathological manifestations, surgical methods, and follow-up results.

CD133 expression may be useful as a prognostic indicator in colorectal cancer, a tool for optimizing therapy and supportive evidence for the cancer stem cell hypothesis: a meta-analysis.

We performed a meta-analysis of CD133-related clinical data to investigate the role of cancer stem cells (CSCs) in the clinical outcomes of colorectal cancer (CRC) patients, analyzing the effectiveness of various therapeutic strategies and examining the validity of the CSC hypothesis. For 28 studies (4546 patients), the relative risk (RR) to survival outcomes associated with CD133+ CRCs were calculated using STATA 12.0 software.