Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours.

Objectives: This study aimed to develop a deep learning radiomic model using multimodal imaging to differentiate benign and malignant breast tumours.

Methods: Multimodality imaging data, including ultrasonography (US), mammography (MG), and magnetic resonance imaging (MRI), from 322 patients (112 with benign breast tumours and 210 with malignant breast tumours) with histopathologically confirmed breast tumours were retrospectively collected between December 2018 and May 2023. Based on multimodal imaging, the experiment was divided into three parts: traditional radiomics, deep learning radiomics, and feature fusion.

Predictive Value of Serum microRNA-29b-3p in Recurrence of Atrial Fibrillation After Radiofrequency Catheter Ablation.

Objective: Atrial fibrillation (AF) is a common arrhythmia. This study explored serum miR-29b-3p expression in AF patients and its value in predicting AF recurrence after radiofrequency catheter ablation (RFCA).

Methods: Totally 100 AF patients who underwent RFCA were enrolled, with 100 individuals without AF as controls.

Benign and malignant classification of breast tumor ultrasound images using conventional radiomics and transfer learning features: A multicenter retrospective study.

This study aims to establish an effective benign and malignant classification model for breast tumor ultrasound images by using conventional radiomics and transfer learning features. We collaborated with a local hospital and collected a base dataset (Dataset A) consisting of 1050 cases of single lesion 2D ultrasound images from patients, with a total of 593 benign and 357 malignant tumor cases. The experimental approach comprises three main parts: conventional radiomics, transfer learning, and feature fusion.

Constructing the Optimal Classification Model for Benign and Malignant Breast Tumors Based on Multifeature Analysis from Multimodal Images.

The purpose of this study was to fuse conventional radiomic and deep features from digital breast tomosynthesis craniocaudal projection (DBT-CC) and ultrasound (US) images to establish a multimodal benign-malignant classification model and evaluate its clinical value. Data were obtained from a total of 487 patients at three centers, each of whom underwent DBT-CC and US examinations. A total of 322 patients from dataset 1 were used to construct the model, while 165 patients from datasets 2 and 3 formed the prospective testing cohort.

Tubeimuside I improves the efficacy of a therapeutic dendritic cell-based vaccine against colorectal cancer.

Introduction: () infection has been confirmed to be associated with the development, chemoresistance, and immune evasion of colorectal cancer (CRC). The complex relationship between the microorganism, host cells, and the immune system throughout all stages of CRC progression, which makes the development of new therapeutic methods difficult.

Methods: We developed a new dendritic cell (DC) vaccine to investigate the antitumor efficacy of CRC immunotherapy strategies.

PSR-Nets: Deep neural networks with prior shift regularization for PET/CT based automatic, accurate, and calibrated whole-body lymphoma segmentation.

Lymphoma is a type of lymphatic tissue originated cancer. Automatic and accurate lymphoma segmentation is critical for its diagnosis and prognosis yet challenging due to the severely class-imbalanced problem. Generally, deep neural networks trained with class-observation-frequency based re-weighting loss functions are used to address this problem.

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images.

Background: The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible.

Neurodevelopmental outcomes of extremely preterm infants in southern China: A multicenter study.

Objective: To assess treatment outcomes and associated factors of extremely preterm infants (EPIs) in GuangXi, China.

Methods: This was a retrospective study consisting of 131 eligible cases with gestational age (GA) between 22 and 28 weeks, and infants were followed until 18-24 months. Data including clinical characteristics, perinatal factors and after-birth conditions were collected from the neonatal intensive care unit in 10 hospitals in Guangxi from January 1st 2010 until May 31st 2016.

A study of 131iodine-labeling of histamine-indomethacin: its in vivo therapeutic effect and anti-tumor mechanisms in Lewis-bearing lung cancer.

Background: In our research,we study the effect of 131iodine-labeled histamine-indomethacin (131I-His-IN). We focus on its in vivo therapeutic effect and anti-tumor mechanisms in Lewis-bearing lung cancer.

Methods: 131I-His-IN was administered by garage to the mice.