Quantitative parameters in novel spectral computed tomography: Assessment of Ki-67 expression in patients with gastric adenocarcinoma.

Background: The level of Ki-67 expression has served as a prognostic factor in gastric cancer. The quantitative parameters based on the novel dual-layer spectral detector computed tomography (DLSDCT) in discriminating the Ki-67 expression status are unclear.

Aim: To investigate the diagnostic ability of DLSDCT-derived parameters for Ki-67 expression status in gastric carcinoma (GC).

Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma.

Objectives: Lymph node (LN) metastasis is an important prognostic factor in rectal cancer (RC). However, accurate identification of LN metastasis can be challenged for radiologists. The aim of our study was to assess the utility of MRI radiomics based on T2-weighted images (T2WI) and amide proton transfer-weighted (APTw) images for predicting LN metastasis in RC preoperatively.

Wall characteristics of atherosclerotic middle cerebral arteries in patients with single or multiple infarcts: A high-resolution MRI Study.

Background And Purpose: Understanding the stroke mechanism of middle cerebral artery (MCA) atherosclerosis may inform secondary prevention. The aim of this study was to explore the relationship between vascular wall characteristics and infarction patterns using high-resolution magnetic resonance imaging (HRMRI) and diffusion-weighted imaging (DWI).

Methods: From November 2018 to March 2021, patients with acute ischemic stroke due to MCA atherosclerotic disease were retrospectively analyzed.

Utility of dual-layer spectral-detector CT imaging for predicting pathological tumor stages and histologic grades of colorectal adenocarcinoma.

Objectives: To assess the utility of Dual-layer spectral-detector CT (DLCT) in predicting the pT stage and histologic grade for colorectal adenocarcinoma (CRAC).

Methods: A total of 131 patients (mean 62.7 ± 12.

Deep learning for screening primary osteopenia and osteoporosis using spine radiographs and patient clinical covariates in a Chinese population.

Purpose: Many high-risk osteopenia and osteoporosis patients remain undiagnosed. We proposed to construct a convolutional neural network model for screening primary osteopenia and osteoporosis based on the lumbar radiographs, and to compare the diagnostic performance of the CNN model adding the clinical covariates with the image model alone.

Methods: A total of 6,908 participants were collected for analysis, including postmenopausal women and men aged 50-95 years, who performed conventional lumbar x-ray examinations and dual-energy x-ray absorptiometry (DXA) examinations within 3 months.

Combining diffusion kurtosis imaging and clinical data for predicting the extramural venous invasion of rectal adenocarcinoma.

Objective: The aim of this study is to investigate the diagnostic ability of diffusion kurtosis imaging (DKI) -derived parameters combining with clinical data as risk factors for EMVI's involvement status in rectal adenocarcinoma.

Materials And Methods: Preoperative MR examination including DKI and conventional diffusion-weighted imaging (DWI) was performed on 154 rectal adenocarcinoma patients enrolled in this respective study. K, D, and apparent diffusion coefficient (ADC) values were calculated.

Predicting Treatment Response of Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Using Amide Proton Transfer MRI Combined With Diffusion-Weighted Imaging.

Objective: To evaluate amide proton weighted (APTw) MRI combined with diffusion-weighted imaging (DWI) in predicting neoadjuvant chemoradiotherapy (NCRT) response in patients with locally advanced rectal cancer (LARC).

Methods: 53 patients with LARC were enrolled in this retrospective study. MR examination including APTw MRI and DWI was performed before and after NCRT.

Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.

Background: It is a permanent challenge to differentiate small solid lung nodules. Massive data, extracted from medical image through radiomics analysis, may help early diagnosis of lung cancer. The aim of this study was to assess the usefulness of a quantitative radiomic model developed from baseline low-dose computed tomography (LDCT) screening for the purpose of predicting malignancy in small solid pulmonary nodules (SSPNs).

Prevalence, Serotypes, and Antimicrobial Resistance Profiles Among Isolated from Food Catering Workers in Nantong, China.

is a worldwide foodborne pathogen causing human disease. Food handlers, who are potential carriers of , may transmit the pathogen to consumers through food. To determine the prevalence of serovars among food handlers working in the catering industry in Nantong, China, a total of 214,542 food handlers' fecal samples were tested for in the Nantong CDC (Centers for Disease Control) from 2012 to 2017.

N400 repetition effect in unidentifiable Chinese characters: evidence for automatic process.

A new matter of debate is whether N400 is exclusively sensitive to automatic or postlexical processes. Recent studies showing N400 modulation by masked primes support an automatic process account. However, these studies cannot directly prove an automatic process.