Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging.

Main Objectives: We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal cancer using endorectal ultrasound imaging.

Methods: This study included 304 patients with rectal cancer from Fujian Medical University Union Hospital. The patients were randomly divided into a training group (213 patients) and a test group (91 patients) at a 7:3 ratio.

Deep graph clustering via aligning representation learning.

Deep graph clustering is a fundamental yet challenging task for graph data analysis. Recent efforts have witnessed significant success in combining autoencoder and graph convolutional network to explore graph-structured data. However, we observe that these approaches tend to map different nodes into the same representation, thus resulting in less discriminative node feature representation and limited clustering performance.

Multi-Level Cross-Modal Interactive-Network-Based Semi-Supervised Multi-Modal Ship Classification.

Ship image classification identifies the type of ships in an input image, which plays a significant role in the marine field. To enhance the ship classification performance, various research focuses on studying multi-modal ship classification, which aims at combining the advantages of visible images and infrared images to capture complementary information. However, the current methods simply concatenate features of different modalities to learn complementary information, which neglects the multi-level correlation between different modalities.

Association between monocyte to high-density lipoprotein cholesterol ratio and telomere length: based on NHANES 1999-2002.

Background: Telomere length is closely associated with the occurrence and development of cardiovascular and other diseases. Monocyte to high-density lipoprotein cholesterol ratio (MHR) is a novel indicator of inflammation, oxidative stress, and metabolic syndrome, with some predictive ability for related disease risks in clinical practice. However, there is no research on the correlation between these two factors.

Association of Obstructive Sleep Apnea on Heart Failure and Its Risk Factors: A Two-Step Mendelian Randomization Study.

Introduction: Recent studies have indicated that obstructive sleep apnea (OSA) is linked to a higher likelihood of heart failure (HF). However, the causal connection between the two conditions is uncertain. We aimed to investigate the causal association of OSA with HF and its risk factors.

Radiogenomic analysis for predicting lymph node metastasis and molecular annotation of radiomic features in pancreatic cancer.

Background: To provide a preoperative prediction model for lymph node metastasis in pancreatic cancer patients and provide molecular information of key radiomic features.

Methods: Two cohorts comprising 151 and 54 pancreatic cancer patients were included in the analysis. Radiomic features from the tumor region of interests were extracted by using PyRadiomics software.

Deep Learning-Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging.

Purpose: To develop a deep neural network system for the automatic segmentation and risk stratification prediction of gastrointestinal stromal tumors (GISTs).

Methods: A total of 980 ultrasound (US) images from 245 GIST patients were retrospectively collected. These images were randomly divided (6:2:2) into a training set, a validation set, and an internal test set.

Shear-wave elastography predicts T-restaging and pathologic complete response of rectal cancer post neoadjuvant chemoradiotherapy.

Purpose: To investigate the value of shear-wave elastography (SWE) in assessing the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer.

Methods: In this study, 455 participants with locally advanced rectal cancer who underwent nCRT at our hospital between September 2021 and December 2022 were prospectively enrolled. The participants were randomly divided into training and test cohorts in a 3:2 ratio.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

Objective: This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC).

Methods: This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software.

Infection of Hilar and Mediastinal Lymph Nodes: A Case Report and Literature Review.

In the available reports on clinical medicine, the infection sites of include wounds, bone marrow, respiratory tract, and catheters. A 61-year-old woman was admitted to our hospital; her hilar and mediastinal lymph nodes were found to be enlarged during health examination, but there was no specific discomfort. Initially, she had undergone a mediastinal lymph node biopsy and pathology, but the diagnosis was not confirmed.

Metagenomic Next-Generation Sequencing Contributes to the Diagnosis of Pneumonia Caused by in a Male Patient: Case Report and Literature Review.

Background: is generally considered as the main cause of ruminants abortion, but it rarely causes human infection resulting in abortion or pneumonia.

Case Presentation: We report a case of male patient with pneumonia caused by . Results of next generation sequencing (NGS) in the bronchoalveolar lavage fluid (BALF) indicated infection.

Use of a Convolutional Neural Network to Predict the Malignant Potential of Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Images: Visualization of the Focus of the Prediction Model.

Objective: We established a deep convolutional neural network (CNN) model based on ultrasound images (US-CNN) for predicting the malignant potential of gastrointestinal stromal tumors (GISTs).

Methods: A total of 980 ultrasound images from 245 pathology-confirmed GIST patients after surgical operation were retrospectively collected and divided into a low (very-low-risk, low-risk) and a high (medium-risk, high-risk) malignant potential group. Eight pre-trained CNN models were used to extract the features.

Deep Multiview Adaptive Clustering With Semantic Invariance.

Multiview clustering has attracted significant attention in various fields, due to the superiority in mining patterns of multiview data. However, previous methods are still confronted with two challenges. First, they do not fully consider the semantic invariance of multiview data in aggregating complementary information, degrading semantic robustness of fusion representations.

Multiview Deep Forest for Overall Survival Prediction in Cancer.

Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper.

Ultrasound radiomics model-based nomogram for predicting the risk Stratification of gastrointestinal stromal tumors.

This study aimed to develop and evaluate a nomogram based on an ultrasound radiomics model to predict the risk grade of gastrointestinal stromal tumors (GISTs). 216 GIST patients pathologically diagnosed between December 2016 and December 2021 were reviewed and divided into a training cohort (n = 163) and a validation cohort (n = 53) in a ratio of 3:1. The tumor region of interest was depicted on each patient's ultrasound image using ITK-SNAP, and the radiomics features were extracted.

LEGAN: A Light and Effective Generative Adversarial Network for medical image synthesis.

Medical image synthesis plays an important role in clinical diagnosis by providing auxiliary pathological information. However, previous methods usually utilize the one-step strategy designed for wild image synthesis, which are not sensitive to local details of tissues within medical images. In addition, these methods consume a great number of computing resources in generating medical images, which seriously limits their applicability in clinical diagnosis.

Two-dimensional shear wave elastography of the pancreas: measurement success rate, repeatability, and factors affecting measurement values.

Purpose: We aimed to evaluate the success rate, repeatability, and factors affecting the measurement values of two-dimensional ultrasonic shear wave elastography (2D-SWE) for measuring pancreatic stiffness.

Methods: This prospective study recruited 100 healthy participants. 2D-SWE was performed on the pancreatic head, body, and tail.

Application of ultrasonography in predicting the biological risk of gastrointestinal stromal tumors.

Objectives: To explore and establish a reliable and noninvasive ultrasound model for predicting the biological risk of gastrointestinal stromal tumors (GISTs).

Materials And Methods: We retrospectively reviewed 266 patients with pathologically-confirmed GISTs and 191 patients were included. Data on patient sex, age, tumor location, biological risk classification, internal echo, echo homogeneity, boundary, shape, blood flow signals, presence of necrotic cystic degeneration, long diameter, and short/long (S/L) diameter ratio were collected.

A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning.

Nowadays, deep representations have been attracting much attention owing to the great performance in various tasks. However, the interpretability of deep representations poses a vast challenge on real-world applications. To alleviate the challenge, a deep matrix factorization method with non-negative constraints is proposed to learn deep part-based representations of interpretability for big data in this paper.

Efficacy and safety of the application of extensive ablation in patients with atrioventricular re-entrant tachycardia (a retrospective study).

Radiofrequency catheter ablation (RFCA) has become the standard effective therapy for supraventricular tachycardia, but the reported success rates of ablation have differed across a large number of single-center studies. The main reason for tachycardia recurrence is accessory pathway (Ap)-mediated tachycardia, and the use of the RFCA strategy may be related to recurrence. This study compared the efficacy and safety of two different RFCA strategies for Ap-mediated tachycardia.

Cross Modal Few-Shot Contextual Transfer for Heterogenous Image Classification.

Deep transfer learning aims at dealing with challenges in new tasks with insufficient samples. However, when it comes to few-shot learning scenarios, due to the low diversity of several known training samples, they are prone to be dominated by specificity, thus leading to one-sidedness local features instead of the reliable global feature of the actual categories they belong to. To alleviate the difficulty, we propose a cross-modal few-shot contextual transfer method that leverages the contextual information as a supplement and learns context awareness transfer in few-shot image classification scenes, which fully utilizes the information in heterogeneous data.