Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study.

Objectives: This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepatocellular carcinoma (HCC).

Methods: Utilizing a retrospective dataset of 546 HCC patients from five centers, we developed and validated a clinical-radiological model and deep learning models aimed at MVI prediction. The models were developed on a dataset of 263 cases consisting of data from three centers, internally validated on a set of 66 patients, and externally tested on two independent sets.

Diagnostic accuracy and pitfalls of MRI for restaging locally advanced rectal cancer in patients following anti-PD1 therapy plus neoadjuvant chemoradiotherapy: a multicenter study.

Purpose: Effectiveness of programmed cell death 1 blockade (anti-PD1) treatment plus neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced rectal cancer and proficient mismatch repair (pMMR-LARC) has been recently proven. However, the role of MRI in tumor restaging following anti-PD1 plus NCRT is less established. This study aims to evaluate the diagnostic performance and challenges of MRI for restaging pMMR-LARC patients after anti-PD1 plus NCRT treatment.

Increased CT Attenuation of Pericolic Adipose Tissue as a Noninvasive Marker of Disease Severity in Ulcerative Colitis.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown. This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity.

Molecular Mechanisms of circKIF4A in Breast Cancer Progression.

Aim: Breast cancer (BC) is the most frequently diagnosed malignancy worldwide, necessitating continued research into its molecular mechanisms. Circular RNAs (circRNAs) are increasingly recognized for their role in various cancers, including BC. This study explores the role of circRNA kinesin family member 4A (circKIF4A) in BC progression and its underlying molecular mechanisms.

State-specific GluCEST alterations in insular subregions are associated with depression and plasma inflammatory biomarker levels in patients with inflammatory bowel disease.

Background: Depression commonly co-occurs with inflammatory bowel disease (IBD). Abnormal glutamate levels in the insula and altered plasma inflammatory biomarkers are observed in IBD and depression. However, the changes in glutamate concentrations in insular subregions in IBD and their relationship with depression and inflammatory markers remain unclear.

Inter-reader agreement of LI-RADS treatment response algorithm among three readers with different seniorities for hepatocellular carcinoma after locoregional therapy.

Background: The accurate evaluation of tumor response after locoregional therapy is crucial for adjusting therapeutic strategy and guiding individualized follow-up.

Purpose: To determine the inter-reader agreement of the LR-TR algorithm for hepatocellular carcinoma treated with locoregional therapy among radiologists with different seniority.

Material And Methods: A total of 275 treated observations on 249 MRI scans from 99 patients were retrospectively collected.

Paracetamol-induced Liver Injury in An Experimental Rat Model: Noninvasive Assessment using DKI and the Effect of Gadoxetate on DKI Parameters.

Purpose: To explore the potential of diffusion kurtosis imaging (DKI) for assessing the degree of liver injury in a paracetamol-induced rat model and to simultaneously investigate the effect of intravenous gadoxetate on DKI parameters.

Methods: Paracetamol was used to induce hepatoxicity in 39 rats. The rats were pathologically classified into 3 groups: normal (n=11), mild necrosis (n=18), and moderate necrosis (n=10).

Computed tomography-based body composition parameters can predict short-term prognosis in ulcerative colitis patients.

Objectives: Emerging evidence suggests a potential relationship between body composition and short-term prognosis of ulcerative colitis (UC). Early and accurate assessment of rapid remission based on conventional therapy via abdominal computed tomography (CT) images has rarely been investigated. This study aimed to build a prediction model using CT-based body composition parameters for UC risk stratification.

Preoperative evaluation of microvascular invasion in hepatocellular carcinoma with a radiological feature-based nomogram: a bi-centre study.

Purpose: To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre.

Method: A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis.

[Screen of key characteristic genes of nasopharyngeal carcinoma (NPC) base on machine learning and analysis of their correlation with immune cells].

Objective Machine learning was used to screen the key characteristic genes of nasopharyngeal carcinoma (NPC) and analyze their correlation with immune cells. Methods Download the NPC training datasets (GSE12452 and GSE13597) and the validation dataset (GSE53819) from the Gene Expression Omnibus (GEO). Firstly, the training data sets were merged and screened for differentially expressed genes (DEGs); Secondly, the DEGs were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and immune cell infiltration analysis.

Corrigendum: Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study.

[This corrects the article DOI: 10.3389/fcvm.2021.

Diagnostic Accuracy of Subtraction Coronary CT Angiography in Severely Calcified Segments: Comparison Between Readers With Different Levels of Experience.

Purpose: Subtraction coronary CT angiography (CCTA) may reduce blooming and beam-hardening artifacts. This study aimed to assess its value in improving the diagnostic accuracy of readers with different experience levels.

Method: We prospectively enrolled patients with target segment who underwent CCTA and invasive coronary angiography (ICA).

Automated segmentation of liver segment on portal venous phase MR images using a 3D convolutional neural network.

Objective: We aim to develop and validate a three-dimensional convolutional neural network (3D-CNN) model for automatic liver segment segmentation on MRI images.

Methods: This retrospective study evaluated an automated method using a deep neural network that was trained, validated, and tested with 367, 157, and 158 portal venous phase MR images, respectively. The Dice similarity coefficient (DSC), mean surface distance (MSD), Hausdorff distance (HD), and volume ratio (RV) were used to quantitatively measure the accuracy of segmentation.

Artificial intelligence stenosis diagnosis in coronary CTA: effect on the performance and consistency of readers with less cardiovascular experience.

Background: To investigate the influence of artificial intelligence (AI) based on deep learning on the diagnostic performance and consistency of inexperienced cardiovascular radiologists.

Methods: We enrolled 196 patents who had undergone both coronary computed tomography angiography (CCTA) and invasive coronary angiography (ICA) within 6 months. Four readers with less cardiovascular experience (Reader 1-Reader 4) and two cardiovascular radiologists (level II, Reader 5 and Reader 6) evaluated all images for ≥ 50% coronary artery stenosis, with ICA as the gold standard.

Diagnostic Accuracy and Generalizability of a Deep Learning-Based Fully Automated Algorithm for Coronary Artery Stenosis Detection on CCTA: A Multi-Centre Registry Study.

In this retrospective, multi-center study, we aimed to estimate the diagnostic accuracy and generalizability of an established deep learning (DL)-based fully automated algorithm in detecting coronary stenosis on coronary computed tomography angiography (CCTA). A total of 527 patients (33.0% female, mean age: 62.

Subtraction improves the accuracy of coronary CT angiography for detecting obstructive disease in severely calcified segments.

Objective: To investigate the accuracy, diagnostic confidence, and interobserver agreement of subtraction coronary CT angiography (CCTA) versus invasive coronary angiography on 320-row CT in coronary segments with severe or non-severe calcification.

Materials/methods: Sixty-four patients (33 men, 66.6 ± 8.

Differential Reorganization of SMA Subregions After Stroke: A Subregional Level Resting-State Functional Connectivity Study.

: The human supplementary motor area (SMA) contains two functional subregions of the SMA proper and preSMA; however, the reorganization patterns of the two SMA subregions after stroke remain uncertain. Meanwhile, a focal subcortical lesion may affect the overall functional reorganization of brain networks. We sought to identify the differential reorganization of the SMA subregions after subcortical stroke using the resting-state functional connectivity (rsFC) analysis.

Altered Coupling Between Resting-State Cerebral Blood Flow and Functional Connectivity in Schizophrenia.

Background: Respective changes in resting-state cerebral blood flow (CBF) and functional connectivity in schizophrenia have been reported. However, their coupling alterations in schizophrenia remain largely unknown.

Methods: 89 schizophrenia patients and 90 sex- and age-matched healthy controls underwent resting-state functional MRI to calculate functional connectivity strength (FCS) and arterial spin labeling imaging to compute CBF.

Combination of volume and perfusion parameters reveals different types of grey matter changes in schizophrenia.

Diverse brain structural and functional changes have been reported in schizophrenia. Identifying different types of brain changes may help to understand the neural mechanisms and to develop reliable biomarkers in schizophrenia. We aimed to categorize different grey matter changes in schizophrenia based on grey matter volume (GMV) and cerebral blood flow (CBF).