首页|Huazhong University of Science and Technology Reports Findings in Rectal Cancer (Machine learning model for prediction of permanent stoma after anterior resecti on of rectal cancer: A multicenter study)
Huazhong University of Science and Technology Reports Findings in Rectal Cancer (Machine learning model for prediction of permanent stoma after anterior resecti on of rectal cancer: A multicenter study)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Oncology - Rectal Canc er is the subject of a report. According to news reporting from Wuhan, People’s Republic of China, by NewsRx journalists, research stated, “The conversion from a temporary to a permanent stoma (PS) following rectal cancer surgery significan tly impacts the quality of life of patients. However, there is currently a lack of practical preoperative tools to predict PS formation.” The news correspondents obtained a quote from the research from the Huazhong Uni versity of Science and Technology, “The purpose of this study is to establish a preoperative predictive model for PS using machine learning algorithms to guide clinical practice. In this retrospective study, we analyzed clinical data from a total of 655 patients who underwent anterior resection for rectal cancer, with 552 patients from one medical center and 103 from another. Through machine learn ing algorithms, five predictive models were developed, and each was thoroughly e valuated for predictive performance. The model with superior predictive accuracy underwent additional validation using both an independent testing cohort and th e external validation cohort. The Shapley Additive exPlanations (SHAP) approach was employed to elucidate the predictive factors influencing the model, providin g an in-depth visual analysis of its decisionmaking process. Eight variables we re selected for the construction of the model. The support vector machine (SVM) model exhibited superior predictive performance in the training set, evidenced b y an AUC of 0.854 (95 % CI:0.803-0.904). This performance was corr oborated in both the testing set and external validation set, where the model de monstrated an AUC of 0.851 (95%CI:0.748-0.954) and 0.815 (95% CI:0.710-0.919), respectively, indicating its efficacy in identifying the PS.”
WuhanPeople’s Republic of ChinaAsiaCancerCyborgsEmerging TechnologiesGastroenterologyHealth and MedicineMachine LearningOncologyRectal CancerSurgery