首页|Qinghai University Affiliated Hospital Reports Findings in Gastric Cancer (Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer)
Qinghai University Affiliated Hospital Reports Findings in Gastric Cancer (Machine learning identifies the risk of complications after laparoscopic radical gastrectomy for gastric cancer)
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Baishideng Publishing Group Inc
New research on Oncology Gastric Cancer is the subject of a report. According to news reporting originating from Xining, People’s Republic of China, by NewsRx correspondents, research stated, “Laparoscopic radical gastrectomy is widely used, and perioperative complications have become a highly concerned issue. To develop a predictive model for complications in laparoscopic radical gastrectomy for gastric cancer to better predict the likelihood of complications in gastric cancer patients within 30 days after surgery, guide perioperative treatment strategies for gastric cancer patients, and prevent serious complications.” Our news editors obtained a quote from the research from Qinghai University Affiliated Hospital, “In total, 998 patients who underwent laparoscopic radical gastrectomy for gastric cancer at 16 Chinese medical centers were included in the training group for the complication model, and 398 patients were included in the validation group. The clinicopathological data and 30-d postoperative complications of gastric cancer patients were collected. Three machine learning methods, lasso regression, random forest, and artificial neural networks, were used to construct postoperative complication prediction models for laparoscopic distal gastrectomy and laparoscopic total gastrectomy, and their prediction efficacy and accuracy were evaluated. The constructed complication model, particularly the random forest model, could better predict serious complications in gastric cancer patients undergoing laparoscopic radical gastrectomy. It exhibited stable performance in external validation and is worthy of further promotion in more centers. Using the risk factors identified in multicenter datasets, highly sensitive risk prediction models for complications following laparoscopic radical gastrectomy were established.”
XiningPeople’s Republic of ChinaAsiaCancerCyborgsDigestive System Surgical ProceduresEmerging TechnologiesEndoscopyGastrectomyGastric CancerGastroenterologyHealth and MedicineLaparoscopyMachine LearningOncologyRisk and PreventionSurgerySurgical Procedures
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Baishideng Publishing Group Inc
