Deep Learning-Based Automatic Segmentation Combined with Radiomics to Predict Post-TACE Liver Failure in HCC Patients.

Objective: To develop and validate a deep learning-based automatic segmentation model and combine with radiomics to predict post-TACE liver failure (PTLF) in hepatocellular carcinoma (HCC) patients.

Methods: This was a retrospective study enrolled 210 TACE-trated HCC patients. Automatic segmentation model based on nnU-Net neural network was developed to segment medical images and assessed by the Dice similarity coefficient (DSC).

Predicting overall survival and prophylactic cranial irradiation benefit in small cell lung cancer patients: a multicenter cohort study.

Background: To construct a CT-based radiomics nomogram, enabling the estimation of overall survival (OS) in small cell lung cancer (SCLC) patients and facilitating the identification of prophylactic cranial irradiation (PCI) beneficiaries through risk stratification using the radiomics score (RS).

Methods: A retrospective recruitment of 375 patients with pathologically confirmed SCLC was conducted across three medical centers, followed by their division into different cohorts. To generate the RS, a series of analyses were performed, including Pearson correlation analysis, univariate Cox analysis, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis.

Computed tomography enterography-based deep learning radiomics to predict stratified healing in patients with Crohn's disease: a multicenter study.

Objectives: This study developed a deep learning radiomics (DLR) model utilizing baseline computed tomography enterography (CTE) to non-invasively predict stratified healing in Crohn's disease (CD) patients following infliximab (IFX) treatment.

Methods: The study included 246 CD patients diagnosed at three hospitals. From the first two hospitals, 202 patients were randomly divided into a training cohort (n = 141) and a testing cohort (n = 61) in a 7:3 ratio.

Heat exposure impacts on urban health: A meta-analysis.

The escalating health risks posed by warm weather in urban areas have become a pressing global public health issue. This study undertakes a meta-analysis to evaluate the impact of warm weather on health in urban settings. We comprehensively searched PubMed, Embase, Scopus, and Web of Science for literature published before September 6, 2023, evaluating evidence quality using the Navigation Guide Criteria.

Prognostic Evaluation for Hepatocellular Carcinoma with Portal Vein Tumor Thrombus Patients Treated with Transarterial Chemoembolization Plus Molecular Targeted Therapies-Development and Validation of the ABPS Score.

Rationale And Objectives: Transarterial chemoembolization (TACE) plus molecular targeted therapies has emerged as the main approach for treating hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT). A robust model for outcome prediction and risk stratification of recommended TACE plus molecular targeted therapies candidates is lacking. We aimed to develop an easy-to-use tool specifically for these patients.

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.

Objective: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images.

Methods: In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.

Predicting overall survival and prophylactic cranial irradiation benefit in small-cell lung cancer with CT-based deep learning: A retrospective multicenter study.

Background And Purpose: To develop a computed tomography (CT)-based deep learning model to predict overall survival (OS) among small-cell lung cancer (SCLC) patients and identify patients who could benefit from prophylactic cranial irradiation (PCI) based on OS signature risk stratification.

Materials And Methods: This study retrospectively included 556 SCLC patients from three medical centers. The training, internal validation, and external validation cohorts comprised 309, 133, and 114 patients, respectively.

Evaluation of Mucosal Healing in Crohn's Disease: Radiomics Models of Intestinal Wall and Mesenteric Fat Based on Dual-Energy CT.

This study aims to assess the effectiveness of radiomics signatures obtained from dual-energy computed tomography enterography (DECTE) in the evaluation of mucosal healing (MH) in patients diagnosed with Crohn's disease (CD). In this study, 106 CD patients with a total of 221 diseased intestinal segments (79 with MH and 142 non-MH) from two medical centers were included and randomly divided into training and testing cohorts at a ratio of 7:3. Radiomics features were extracted from the enteric phase iodine maps and 40-kev and 70-kev virtual monoenergetic images (VMIs) of the diseased intestinal segments, as well as from mesenteric fat.

Phased secondary small interfering RNAs in var. .

Phased secondary small interfering RNAs (phasiRNAs) in plants play important roles in regulating genome stability, plant development and stress adaption. var. has immense economic, medicinal and cultural significance.

Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn's Disease.

Background: The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity.

Methods: This was a retrospective study with 2 sets of CTE image data.

A comparative study of the predictive value of four models for death in patients with severe burns.

Objective: To assess the prognostic value of the Ryan score, Belgian Outcome of Burn Injury (BOBI) score,revised Baux (rBaux) score, and a new model (a Logit(P)-based scoring method created in 2020) for predicting mortality risk in patients with extremely severe burns and to conduct a comparative analysis.

Methods: A retrospective analysis was conducted on 599 burn patients who met the inclusion criteria and were admitted to the burn unit of the First Affiliated Hospital of Nanchang University from 2017 to 2022. Relevant information was collected, and receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were plotted for each of the four models in assessing mortality in these burn patients using both age-stratified and unstratified forms.

A multi-institutional study to predict the benefits of DEB-TACE and molecular targeted agent sequential therapy in unresectable hepatocellular carcinoma using a radiological-clinical nomogram.

Objective: Exploring the efficacy of a Radiological-Clinical (Rad-Clinical) model in predicting prognosis of unresectable hepatocellular carcinoma (HCC) patients after drug eluting beads transcatheter arterial chemoembolization (DEB-TACE) to optimize the targeted sequential treatment.

Methods: In this retrospective analysis, we included 202 patients with unresectable HCC who received DEB-TACE treatment in 17 institutions from June 2018 to December 2022. Progression-free survival (PFS)-related radiomics features were computationally extracted from HCC patients to build a radiological signature (Rad-signature) model with least absolute shrinkage and selection operator regression.

Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma.

Background: Small (< 4 cm) clear cell renal cell carcinoma (ccRCC) is the most common type of small renal cancer and its prognosis is poor. However, conventional radiological characteristics obtained by computed tomography (CT) are not sufficient to predict the nuclear grade of small ccRCC before surgery.

Methods: A total of 113 patients with histologically confirmed ccRCC were randomly assigned to the training set (n = 67) and the testing set (n = 46).

A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study.

Purpose: To develop a radiomics nomogram based on computed tomography (CT) to estimate progression-free survival (PFS) in patients with small cell lung cancer (SCLC) and assess its incremental value to the clinical risk factors for individual PFS estimation.

Methods: 558 patients with pathologically confirmed SCLC were retrospectively recruited from three medical centers. A radiomics signature was generated by using the Pearson correlation analysis, univariate Cox analysis, and multivariate Cox analysis.

Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions.

Objectives: To establish a clinical radiomics nomogram that differentiates malignant and non-malignant pleural effusions.

Methods: A total of 146 patients with malignant pleural effusion (MPE) and 93 patients with non-MPE (NMPE) were included. The ROI image features of chest lesions were extracted using CT.

Erratum to "Development and validation of a clinical radiomics nomogram to predict secondary loss of response to infliximab in Crohn's disease patients" [Heliyon 9(4) (April 2023) e14594].

[This corrects the article DOI: 10.1016/j.heliyon.

Evaluation of symptomatic small bowel stricture in Crohn's disease by double-balloon endoscopy.

Purpose: To assess the efficacy of double-balloon endoscopy (DBE) for the detection of small-bowel strictures in Crohn's disease (CD).

Methods: This tertiary-referral hospital cohort study was conducted between January 2018 and May 2022. CD patients with symptoms of small-bowel stricture were enrolled sequentially.

CTE-Based Radiomics Models Can Identify Mucosal Healing in Patients with Crohn's Disease.

Rationale And Objectives: To develop computed tomography enterography (CTE)-based radiomics models to assess mucosal healing (MH) in patients with Crohn's disease (CD).

Materials And Methods: CTE images were retrospectively collected from 92 confirmed cases of CD at the post-treatment review. Patients were randomly divided into developing (n = 73) and testing (n = 19) groups.

Development and validation of a clinical radiomics nomogram to predict secondary loss of response to infliximab in Crohn's disease patients.

Background: Infliximab (IFX) is the first-line treatment for Crohn's disease (CD). However, the secondary loss of response (LOR) is common in IFX therapy. Therefore, non-invasive assessment of LOR in CD patients is the goal pursued by clinicians.

Mucosal healing assessment in Crohn's disease with normalized iodine concentration from dual-energy CT enterography: comparison with endoscopy.

Objectives: Mucosal healing (MH) is an important goal in the treatment of patients with Crohn's disease (CD). Noninvasive assessment of MH with normalized iodine concentration (NIC) is unknown.

Methods: In this retrospective study, 94 patients with diagnosed CD underwent endoscopy and dual-energy CT enterography (DECTE) at the post-infliximab treatment review.

Characterizing microRNA editing and mutation sites in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder whose pathogenesis is still unclear. MicroRNAs (miRNAs) are a kind of endogenous small non-coding RNAs that play important roles in the post-transcriptional regulation of genes. Recent researches show that miRNAs are edited in multiple ways especially in central nervous systems.