Comparative efficacy and safety of Chinese medicine injections combined with capecitabine and oxaliplatin chemotherapies in treatment of colorectal cancer: A bayesian network meta-analysis.

The aim of the present Bayesian network meta-analysis (NMA) was to explore the comparative effectiveness and safeaty of different Chinese Medicine injections (CMIs) combined with the XELOX regimen XELOX alone for colorectal cancer (CRC). A comprehensive search for randomized controlled trials (RCTs) was performed with regard to different CMIs for the treatment of CRC in several electronic databases up to April 2022. The quality assessment of the included RCTs was conducted according to the Cochrane risk of bias tool.

Deep Learning-Based Stage-Wise Risk Stratification for Early Lung Adenocarcinoma in CT Images: A Multi-Center Study.

This study aims to develop a deep neural network (DNN)-based two-stage risk stratification model for early lung adenocarcinomas in CT images, and investigate the performance compared with practicing radiologists. A total of 2393 GGNs were retrospectively collected from 2105 patients in four centers. All the pathologic results of GGNs were obtained from surgically resected specimens.

[A Cross-sectional Study of Osteoporosis and Cardiovascular Calcification in Patients with Chronic Kidney Disease at Different CKD Stages].

Objective: To investigate the status of osteoporosis and cardiovascular calcification in patients with chronic kidney disease (CKD) with different stages, and analyze the correlation between the stages and markers of bone metabolism To correlation.

Methods: A total of 368 CKD patients at stage 3-5 who were treated in First Affiliated Hospital Affiliate to Chongqing Medical University and Chongqing Fuling Central Hospital from July 2017 to January 2018 were enrolled. A total of 60 healthy people who underwent physical examination in the hospital during the same period were enrolled as control group.

Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.

Objectives: This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), and fuse deep leaning and radiomics imaging features to improve the classification performance.

Methods: We first retrospectively collected 513 surgery histopathology confirmed GGNs from two centers. Among these GGNs, 100 were benign and 413 were malignant.