Value of contrast-enhanced CT based radiomic machine learning algorithm in differentiating gastrointestinal stromal tumors with KIT exon 11 mutation: a two-center study.

PURPOSE Knowing the genetic phenotype of gastrointestinal stromal tumors (GISTs) is essential for patients who receive therapy with tyrosine kinase inhibitors. The aim of this study was to develop a radiomic algorithm for predicting GISTs with KIT exon 11 mutation. METHODS We enrolled 106 patients (80 in the training set, 26 in the validation set) with clinicopathologically confirmed GISTs from two centers.

Bioinformatic analysis of potential hub genes in gastric adenocarcinoma.

Gastric adenocarcinoma is the most common histologic type of gastric cancer; however, the pathogenic mechanisms remain unclear. To improve mechanistic understanding and identify new treatment targets or diagnostic biomarkers, we used bioinformatic tools to predict the hub genes related to the process of gastric adenocarcinoma development from public datasets, and explored their prognostic significance. We screened differentially expressed genes between gastric adenocarcinoma and normal gastric tissues in Gene Expression Omnibus datasets (GSE79973, GSE118916, and GSE29998) using the GEO2R tool, and their functions were annotated with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment analyses in the DAVID database.

Construction of a clinical survival prognostic model for middle-aged and elderly patients with stage III rectal adenocarcinoma.

Background: Nomograms for prognosis prediction in colorectal cancer patients are few, and prognostic indicators differ with age.

Aim: To construct a new nomogram survival prediction tool for middle-aged and elderly patients with stage III rectal adenocarcinoma.

Methods: A total of 2773 eligible patients were divided into the training cohort (70%) and the validation cohort (30%).

Application of three-dimensional reconstruction with a Hisense computer-assisted system in upper pancreatic lymph node dissection during laparoscopic-assisted radical gastrectomy.

Background/objective: To investigate the feasibility of three-dimensional (3D) reconstruction with an interactive Hisense computer-assisted system (CAS) for preoperative planning and intraoperative guidance during laparoscopic-assisted upper pancreatic lymph node dissection in distal gastrectomy for gastric cancer.

Methods: This study included 28 patients who underwent preoperative 3D reconstruction of the upper border of the pancreas using Hisense CAS (3D reconstruction group) for preoperative planning and intraoperative navigation. To determine its efficacy, the clinical data of these patients were compared with those of 28 patients who did not undergo 3D reconstruction (control group).

Prediction of cancer-specific survival and overall survival in middle-aged and older patients with rectal adenocarcinoma using a nomogram model.

Objective: To develop a new nomogram tool for predicting survival in middle-aged and elderly patients with rectal adenocarcinoma.

Methods: A total of 6,116 patients were randomly assigned in a 7:3 ratio to training and validation cohorts. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS) in the training set, and two nomogram prognostic models were constructed.