Turning attention to tumor-host interface and focus on the peritumoral heterogeneity of glioblastoma.

Approximately 90% of glioblastoma recurrences occur in the peritumoral brain zone (PBZ), while the spatial heterogeneity of the PBZ is not well studied. In this study, two PBZ tissues and one tumor tissue sample are obtained from each patient via preoperative imaging. We assess the microenvironment and the characteristics of infiltrating immune/tumor cells using various techniques.

Point-of-care ultrasound in the diagnosis and treatment of Budd-Chiari syndrome: A rare case report and literature review.

Budd-Chiari syndrome (BCS) is a life-threatening disease characterized by the partial or complete obstruction of hepatic venous outflow anywhere from the liver to the heart. In China, secondary BCS is rare. We present a case of secondary BCS caused by compression of the suprahepatic inferior vena cava (IVC), mainly due to local bile accumulation in the caudate lobe of the liver.

Predicting the clinical prognosis of acute ischemic stroke using machine learning: an application of radiomic biomarkers on non-contrast CT after intravascular interventional treatment.

Purpose: This study aimed to develop a radiomic model based on non-contrast computed tomography (NCCT) after interventional treatment to predict the clinical prognosis of acute ischemic stroke (AIS) with large vessel occlusion.

Methods: We retrospectively collected 141 cases of AIS from 2016 to 2020 and analyzed the patients' clinical data as well as NCCT data after interventional treatment. Then, the total dataset was divided into training and testing sets according to the subject serial number.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer's disease.

Objective: To identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI.

Methods: A total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period.

Pattern of heterogeneity in normal brain ageing: screening for mild cognitive impairment and its risk of progression with a radiomics model.

Purpose: This study aimed to develop a normal brain ageing model based on magnetic resonance imaging and radiomics, therefore identifying radscore, an imaging indicator representing white matter heterogeneity and exploring the significance of radscore in detecting people's cognitive changes.

Methods: Three hundred sixty cognitively normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 105 CN subjects from the Parkinson's Progression Markers Initiative database were used to develop the model. In ADNI, 230 mild cognitive impairment (MCI) subjects were matched with 230 CN old-aged subjects to evaluate their heterogeneity difference.

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study.

Objective: To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD).

Methods: This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model.

Predicting Alzheimer's progression in MCI: a DTI-based white matter network model.

Objective: This study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer's disease (AD) in MCI patients.

Methods: This study enrolled 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up.

Comparison between morphometry and radiomics: detecting normal brain aging based on grey matter.

Objective: Voxel-based morphometry (VBM), surface-based morphometry (SBM), and radiomics are widely used in the field of neuroimage analysis, while it is still unclear that the performance comparison between traditional morphometry and emerging radiomics methods in diagnosing brain aging. In this study, we aimed to develop a VBM-SBM model and a radiomics model for brain aging based on cognitively normal (CN) individuals and compare their performance to explore both methods' strengths, weaknesses, and relationships.

Methods: 967 CN participants were included in this study.

Disastrous cerebral and ocular vascular complications after cosmetic facial filler injections: a retrospective case series study.

Soft tissue filler injections are among the most popular facial rejuvenation methods. Cerebral infarction and ophthalmic artery occlusion are rare and catastrophic complications, especially when facial cosmetic fillers are injected by inexperienced doctors. Radiologists and plastic surgeons need to increase their awareness of the complications associated with fillers, which allows early diagnosis and intervention to improve patient prognosis.

Hybrid model of CT-fractional flow reserve, pericoronary fat attenuation index and radiomics for predicting the progression of WMH: a dual-center pilot study.

Objective: To develop and validate a hybrid model incorporating CT-fractional flow reserve (CT-FFR), pericoronary fat attenuation index (pFAI), and radiomics signatures for predicting progression of white matter hyperintensity (WMH).

Methods: A total of 226 patients who received coronary computer tomography angiography (CCTA) and brain magnetic resonance imaging from two hospitals were divided into a training set ( = 116), an internal validation set ( = 30), and an external validation set ( = 80). Patients who experienced progression of WMH were identified from subsequent MRI results.

Using mixed reality technique combines multimodal imaging signatures to adjuvant glioma photodynamic therapy.

Background: Photodynamic therapy (PDT) promotes significant tumor regression and extends the lifetime of patients. The actual operation of PDT often relies on the subjective judgment of experienced neurosurgeons. Patients can benefit more from precisely targeting PDT's key operating zones.

RhFGF21 Protects Epidermal Cells against UVB-Induced Apoptosis through Activating AMPK-Mediated Autophagy.

Ultraviolet irradiation, especially ultraviolet B (UVB) irradiation, increases the risks of various skin diseases, such as sunburn, photo-aging and cancer. However, few drugs are available to treat skin lesions. Therefore, the discovery of drugs to improve the health of irradiated skin is urgently needed.

Multilayer perceptron-based prediction of stroke mimics in prehospital triage.

The identification of stroke mimics (SMs) in patients with stroke could lead to delayed diagnosis and waste of medical resources. Multilayer perceptron (MLP) was proved to be an accurate tool for clinical applications. However, MLP haven't been applied in patients with suspected stroke onset within 24 h.

Overall survival time prediction for glioblastoma using multimodal deep KNN.

Glioblastoma (GBM) is a severe malignant brain tumor with bad prognosis, and overall survival (OS) time prediction is of great clinical value for customized treatment. Recently, many deep learning (DL) based methods have been proposed, and most of them build deep networks to directly map pre-operative images of patients to the OS time. However, such end-to-end prediction is sensitive to data inconsistency and noise.

Fibroblast-Conditioned Media Enhance the Yield of Microglia Isolated from Mixed Glial Cultures.

Microglia are the main immune cells of the central nervous system (CNS) and comprise various model systems used to investigate inflammatory mechanisms in CNS disorders. Currently, shaking and mild trypsinization are widely used microglial culture methods; however, the problems with culturing microglia include low yield and a time-consuming process. In this study, we replaced normal culture media (NM) with media containing 25% fibroblast-conditioned media (F-CM) to culture mixed glia and compared microglia obtained by these two methods.

Comparing the Performance of Two Radiomic Models to Predict Progression and Progression Speed of White Matter Hyperintensities.

The aim of this study was to compare two radiomic models in predicting the progression of white matter hyperintensity (WMH) and the speed of progression from conventional magnetic resonance images. In this study, 232 people were retrospectively analyzed at Medical Center A (training and testing groups) and Medical Center B (external validation group). A visual rating scale was used to divide all patients into WMH progression and non-progression groups.

Predictive Value of Pretreatment Lymphocyte-to-Monocyte Ratio and Platelet-to-Lymphocyte Ratio in the Survival of Nasopharyngeal Carcinoma Patients.

Objective: The present study aimed to investigate the predictive value of some indexes, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic inflammatory response index (SIRI), and systemic immune-inflammatory index (SII) in the survival of nasopharyngeal carcinoma (NPC) and provide reference for the treatment.

Methods: A retrospective analysis was performed on 216 patients from 2016 to 2018. The cutoff values of these indexes were determined by the receiver operating characteristic (ROC) curve.

A New Classification System for Postinterventional Cerebral Hyperdensity: The Influence on Hemorrhagic Transformation and Clinical Prognosis in Acute Stroke.

Background: Postinterventional cerebral hyperdensity (PCHD) is commonly seen in acute ischemic patients after mechanical thrombectomy. We propose a new classification of PCHD to investigate its correlation with hemorrhagic transformation (HT). The clinical prognosis of PCHD was further studied.

Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer.

Objectives: To compare multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion (EMVI) in rectal cancer using different machine learning algorithms and to develop and validate the best diagnostic model.

Methods: We retrospectively analyzed 317 patients with rectal cancer. Of these, 114 were EMVI positive and 203 were EMVI negative.

Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model.

Objective: This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD).

Methods: 357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group.

Early Warning Information for Severe and Critical Patients With COVID-19 Based on Quantitative CT Analysis of Lung Segments.

The coronavirus disease 2019 (COVID-19) outbreak is spreading rapidly around the world. We aimed to explore early warning information for patients with severe/critical COVID-19 based on quantitative analysis of chest CT images at the lung segment level. A dataset of 81 patients with coronavirus disease 2019 (COVID-19) treated at Wuhan Wuchang hospital in Wuhan city from 21 January 2020 to 14 February 2020 was retrospectively analyzed, including ordinary and severe/critical cases.

The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland.

Objective: This study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and malignant parotid gland (PG) tumors multicentre cohorts.

Materials And Methods: A cohort of 87 PG tumor patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single tumor region of interest (ROI) of each patient on each phase of CT images.

The Nomogram of MRI-based Radiomics with Complementary Visual Features by Machine Learning Improves Stratification of Glioblastoma Patients: A Multicenter Study.

Background: Glioblastomas (GBMs) represent both the most common and the most highly malignant primary brain tumors. The subjective visual imaging features from MRI make it challenging to predict the overall survival (OS) of GBM. Radiomics can quantify image features objectively as an emerging technique.

An Integrative Nomogram for Identifying Early-Stage Parkinson's Disease Using Non-motor Symptoms and White Matter-Based Radiomics Biomarkers From Whole-Brain MRI.

To develop and validate an integrative nomogram based on white matter (WM) radiomics biomarkers and nonmotor symptoms for the identification of early-stage Parkinson's disease (PD). The brain magnetic resonance imaging (MRI) and clinical characteristics of 336 subjects, including 168 patients with PD, were collected from the Parkinson's Progress Markers Initiative (PPMI) database. All subjects were randomly divided into training and test sets.