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. Preoperative and postoperative clinical characteristics were selected and analyzed to construct the clinical model. Arterial phase, venous phase, delayed phase, and tri-phase combined radiomics algorithms were generated from the training set based on contrast-enhanced computed tomography (CE-CT) images. Various radiomics feature selection methods were used, namely least absolute shrinkage and selection operator (LASSO); minimum redundancy maximum relevance (mRMR); and generalized linear model (GLM) as a machine-learning classifier. Independent predictive factors were determined to construct preoperative and postoperative radiomics nomograms by multivariate logistic regression analysis. The performances of the clinical model, radiomics algorithm, and radiomics nomogram in distinguishing GISTs with the KIT exon 11 mutation were evaluated by area under the curve (AUC) of the receiver operating characteristics. RESULTS Of 106 patients who underwent genetic analysis, 61 had the KIT exon 11 mutation. The combined radiomics algorithm was found to be the best prediction model for differentiating the expression status of the KIT exon 11 mutation (AUC = 0.836; 95% confidence interval [CI], 0.640-0.951) in the validation set. The clinical model, and preoperative and postoperative radiomics nomograms had AUCs of 0.606 (95% CI, 0.397-0.790), 0.715 (95% CI, 0.506-0.873), and 0.679 (95% CI, 0.468-0.847), respectively, with the validation set. CONCLUSION The radiomics algorithm could distinguish GISTs with the KIT exon 11 mutation based on CE-CT images and could potentially be used for selective genetic analysis to support the precision medicine of GISTs.

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. Hub genes were identified based on the protein-protein network constructed in the STRING database with Cytoscape software. A total of 10 hub genes were selected for further analysis, and their expression patterns in gastric adenocarcinoma patients were investigated using the Oncomine GEPIA database. The expression levels of ATP4A, CA9, FGA, ALDH1A1, and GHRL were reduced, whereas those of TIMP1, SPP1, CXCL8, THY1, and COL1A1 were increased in gastric adenocarcinoma. The Kaplan-Meier online plotter tool showed associations of all hub genes except for CA9 with prognosis in gastric adenocarcinoma patients; CXCL8 and ALDH1A1 were positively correlated with survival, and the other genes were negatively correlated with survival. These 10 hub genes may be involved in important processes in gastric adenocarcinoma development, providing new directions for research to clarify the role of these genes and offer insight for improved treatment.

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%). Optimal cutoff values were calculated using the X-tile software for continuous variables. Univariate and multivariate Cox proportional hazards regression analyses were used to determine overall survival (OS) and cancer-specific survival (CSS)-related prognostic factors. Two nomograms were successfully constructed. The discriminant and predictive ability and clinical usefulness of the model were also assessed by multiple methods of analysis. RESULTS: The 95%CI in the training group was 0.719 (0.690-0.749) and 0.733 (0.702-0.74), while that in the validation group was 0.739 (0.696-0.782) and 0.750 (0.701-0.800) for the OS and CSS nomogram prediction models, respectively. In the validation group, the AUC of the three-year survival rate was 0.762 and 0.770, while the AUC of the five-year survival rate was 0.722 and 0.744 for the OS and CSS nomograms, respectively. The nomogram distinguishes all-cause mortality from cancer-specific mortality in patients with different risk grades. The time-dependent AUC and decision curve analysis showed that the nomogram had good clinical predictive ability and decision efficacy and was significantly better than the tumor-node-metastases staging system. CONCLUSION: The survival prediction model constructed in this study is helpful in evaluating the prognosis of patients and can aid physicians in clinical diagnosis and treatment.

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). RESULTS: Fifty-six cases of laparoscopic-assisted distal gastrectomy were performed. Three-dimensional reconstruction was successful in all the patients in the 3D reconstruction group, and real-time navigation was performed during the operation. The rate of correspondence between the 3D reconstruction images and intraoperative findings was 100%. The time taken for upper pancreatic lymph node dissection, number of upper pancreatic lymph node dissections, and number of unnecessary injuries during surgery were superior in the 3D reconstruction group than in the control group. The results of the remaining parameters were not statistically significant. CONCLUSION: Preoperative planning with interactive Hisense CAS 3D reconstruction technology can improve surgeons' understanding of each patient's individual anatomy and can reveal anatomical variations, which is helpful for accurate preoperative planning and intraoperative navigation. This technique is helpful for the implementation of the precise dissection of lymph nodes at the upper edge of the pancreas and improves the quality and safety of the surgery.

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. The validity, accuracy, discrimination, predictive ability, and clinical utility of the models were assessed based on the concordance index (C-index), area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), Kaplan-Meier survival curve, and decision curve analyses. RESULTS: Predictors of OS and CSS were identified, and nomograms were successfully constructed. The calibration discrimination for both the OS and CSS nomogram prediction models was good (C-index: 0.763 and 0.787, respectively). The AUC showed excellent predictive performance, and the calibration curve exhibited significant predictive power for both nomograms. The time-dependent AUC showed that the predictive ability of the predictor-based nomogram was better than that of the TNM stage. The nomograms successfully discriminated high-, medium-, and low-risk patients for all-cause and cancer-specific mortality. The decision curve demonstrated that the nomograms are useful with respect to good decision power. CONCLUSION: Our nomogram survival prediction models may aid in evaluating the prognosis of middle-aged and older patients with rectal adenocarcinoma and guiding the selection of the clinical treatment measures.