查看更多>>摘要:By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New research on Oncology - Colon Cancer is the su bject of a report. According to news reporting originating in Wuxi, People’s Rep ublic of China, by NewsRx journalists, research stated, “Bone metastasis (BM) oc curs when colon cancer cells disseminate from the primary tumor site to the skel etal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient.” The news reporters obtained a quote from the research, “This study’s primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with rightsided colon cancer undergoing complete m esocolonectomy (CME). The study cohort comprised 1,151 individuals diagnosed wit h right-sided colon cancer, with a subset of 73 patients presenting with bone me tastases originating from the colon. We used univariate and multivariate regress ion analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and sur gical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and knearest neighbor algorithm (KNN), to develop the predictiv e model. Additionally, the model was assessed using receiver operating character istic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the m odel. The XGBoost algorithm performed the best performance among the four predic tion models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953-0.994), an accuracy of 0.925 (0.913-0.936), a sensit ivity of 0.921 (0.902-0.940), and a specificity of 0.908 (0.894-0.922). In the v alidation set, the XGBoost algorithm had an AUC value of 0.922 (0.833-0.995), an accuracy of 0.908 (0.889-0.926), a sensitivity of 0.924 (0.873-0.975), and a sp ecificity of 0.883 (0.810-0.956). Furthermore, the AUC value of 0.83 for the ext ernal validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline ph osphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophilto- lymphocyte ratio (NLR) levels as sig nificant risk factors for BM from right-sided colon cancer subsequent to CME.”