One 3D VOI-based deep learning radiomics strategy, clinical model and radiologists for predicting lymph node metastases in pancreatic ductal adenocarcinoma based on multiphasic contrast-enhanced computer tomography.

Purpose: We designed to construct one 3D VOI-based deep learning radiomics strategy for identifying lymph node metastases (LNM) in pancreatic ductal adenocarcinoma on the basis of multiphasic contrast-enhanced computer tomography and to assist clinical decision-making.

Methods: This retrospective research enrolled 139 PDAC patients undergoing pre-operative arterial phase and venous phase scanning examination between 2015 and 2021. A primary group (training group and validation group) and an independent test group were divided.

Deep learning combined with radiomics for the classification of enlarged cervical lymph nodes.

Purpose: To investigate the application of deep learning combined with traditional radiomics methods for classifying enlarged cervical lymph nodes.

Methods: The clinical and computed tomography (CT) imaging data of 276 patients with enlarged cervical lymph nodes (150 with lymph-node metastasis, 65 with lymphoma, and 61 with benign lymphadenopathy) who were treated at the hospital from January 2015 to January 2021 were retrospectively analysed. The patients were randomly divided into a training group and a test group at a ratio of 8:2.

The complete chloroplast genome of (Wilson) (hypanceae, bryophyta).

is a widely distributed moss in East Asia. In this study, Illumina sequencing data was used to assemble the complete chloroplast genome of . The length of the circular genome is 124,037 bp.

Seed germination ecology of Conyza sumatrensis populations stemming from different habitats and implications for management.

Conyza sumatrensis (Retz.) E. H.

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning.

Background: Computational aid for diagnosis based on convolutional neural network (CNN) is promising to improve clinical diagnostic performance. Therefore, we applied pretrained CNN models in multiparametric magnetic resonance (MR) images to classify glioma mimicking encephalitis and encephalitis.

Methods: A data set containing 3064 MRI brain images from 164 patients with a final diagnosis of glioma (n = 56) and encephalitis (n = 108) patients and divided into training and testing sets.

One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC.

An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radiomics has been studied for the prediction of carcinoma characteristics in medical images. The success of previous researches in radiomics is attributed to the availability of annotated all-slice medical images.

CT-Based Radiomics Signature for the Preoperative Discrimination Between Head and Neck Squamous Cell Carcinoma Grades.

Radiomics has been widely used to non-invasively mine quantitative information from medical images and could potentially predict tumor phenotypes. Pathologic grade is considered a predictive prognostic factor for head and neck squamous cell carcinoma (HNSCC) patients. A preoperative histological assessment can be important in the clinical management of patients.

Multiphase contrast-enhanced computed tomography imaging features of salivary duct carcinoma: differentiation from other salivary gland malignancies.

Objective: The aim of this study was to investigate the imaging features of salivary duct carcinoma (SDC) with multiphase contrast-enhanced computed tomography (CECT) and to compare them with those of mucoepidermoid carcinoma (MEC), adenoid cystic carcinoma (ACC), and acinic cell carcinoma.

Study Design: A total of 63 patients with histologically diagnosed salivary gland malignancies underwent preoperative multiphase CECT. Clinical information, location, size, mass pattern, enhancement pattern, borders, invasion of adjacent tissues, and lymph node metastasis were evaluated.