BACKGROUND Despite the promising prospects of utilizing artificial intelligence and machine learning(ML)for comprehensive disease analysis,few models constructed have been applied in clinical practice due to their com...BACKGROUND Despite the promising prospects of utilizing artificial intelligence and machine learning(ML)for comprehensive disease analysis,few models constructed have been applied in clinical practice due to their complexity and the lack of reasonable explanations.In contrast to previous studies with small sample sizes and limited model interpretability,we developed a transparent eXtreme Gradient Boosting(XGBoost)-based model supported by multi-center data,using patients'basic information and clinical indicators to forecast the occurrence of anastomotic leakage(AL)after rectal cancer resection surgery.The model demonstrated robust predictive performance and identified clinically relevant thresholds,which may assist physicians in optimizing perioperative management.AIM To develop an interpretable ML model for accurately predicting the occurrence probability of AL after rectal cancer resection and define our clinical alert values for serum calcium ions.METHODS Patients who underwent anterior resection of the rectum for rectal carcinoma at the Department of Digestive Surgery,Xijing Hospital of Digestive Diseases,Air Force Medical University,and Shaanxi Provincial People's Hospital,were retrospectively collected from January 2011 to December 2021.Ten ML models were integrated to analyze the data and develop the predictive models.Receiver operating characteristic(ROC)curves,calibration curve,decision curve analysis,accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and F1 score were used to evaluate model performance.We employed the SHapley Additive exPlanations(SHAP)algorithm to explain the feature importance of the optimal model.RESULTS A total of ten features were integrated to construct the predictive model and identify the optimal model.XGBoost was considered the best-performing model with an area under the ROC curve(AUC)of 0.984(95%confidence interval:0.972-0.996)in the test set(accuracy:0.925;sensitivity:0.92;specificity:0.927).Furthermore,the model achieved an AUC of 0.703 in external validation.The interpretable SHAP algorithm revealed that the serum calcium ion level was the crucial factor influencing the predictions of the model.CONCLUSION A superior predictive model,leveraging clinical data,has been crafted by employing the most effective XGBoost from a selection of ten algorithms.This model,by predicting the occurrence of AL in patients after rectal cancer resection,has identified the significant role of serum calcium ion levels,providing guidance for clinical practice.The integration of SHAP provides a clear interpretation of the model's predictions.展开更多
BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machin...BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machine learning algorithms in predicting the occurrence of PSH in patients undergoing preventive ostomy after rectal cancer resection,providing valuable support for clinical decision-making.METHODS A retrospective analysis was conducted on the clinical data of 579 patients who underwent rectal cancer resection with preventive ostomy at Tongji Hospital,Huazhong University of Science and Technology,between January 2015 and June 2023.Various machine learning models were constructed and trained using preoperative and intraoperative clinical variables to assess their predictive performance for PSH risk.SHapley Additive exPlanations(SHAP)were used to analyze the importance of features in the models.RESULTS A total of 579 patients were included,with 31(5.3%)developing PSH.Among the machine learning models,the random forest(RF)model showed the best performance.In the test set,the RF model achieved an area under the curve of 0.900,sensitivity of 0.900,and specificity of 0.725.SHAP analysis revealed that tumor distance from the anal verge,body mass index,and preoperative hypertension were the key factors influencing the occurrence of PSH.CONCLUSION Machine learning,particularly the RF model,demonstrates high accuracy and reliability in predicting PSH after preventive ostomy in rectal cancer patients.This technology supports personalized risk assessment and postoperative management,showing significant potential for clinical application.An online predictive platform based on the RF model(https://yangsu2023.shinyapps.io/parastomal_hernia/)has been developed to assist in early screening and intervention for high-risk patients,further enhancing postoperative management and improving patients’quality of life.展开更多
基金Supported by National Natural Science Foundation of China,No.82172781Shaanxi Health Scientific Research Innovation Team Project,No.2024TD-06.
文摘BACKGROUND Despite the promising prospects of utilizing artificial intelligence and machine learning(ML)for comprehensive disease analysis,few models constructed have been applied in clinical practice due to their complexity and the lack of reasonable explanations.In contrast to previous studies with small sample sizes and limited model interpretability,we developed a transparent eXtreme Gradient Boosting(XGBoost)-based model supported by multi-center data,using patients'basic information and clinical indicators to forecast the occurrence of anastomotic leakage(AL)after rectal cancer resection surgery.The model demonstrated robust predictive performance and identified clinically relevant thresholds,which may assist physicians in optimizing perioperative management.AIM To develop an interpretable ML model for accurately predicting the occurrence probability of AL after rectal cancer resection and define our clinical alert values for serum calcium ions.METHODS Patients who underwent anterior resection of the rectum for rectal carcinoma at the Department of Digestive Surgery,Xijing Hospital of Digestive Diseases,Air Force Medical University,and Shaanxi Provincial People's Hospital,were retrospectively collected from January 2011 to December 2021.Ten ML models were integrated to analyze the data and develop the predictive models.Receiver operating characteristic(ROC)curves,calibration curve,decision curve analysis,accuracy,sensitivity,specificity,positive predictive value,negative predictive value,and F1 score were used to evaluate model performance.We employed the SHapley Additive exPlanations(SHAP)algorithm to explain the feature importance of the optimal model.RESULTS A total of ten features were integrated to construct the predictive model and identify the optimal model.XGBoost was considered the best-performing model with an area under the ROC curve(AUC)of 0.984(95%confidence interval:0.972-0.996)in the test set(accuracy:0.925;sensitivity:0.92;specificity:0.927).Furthermore,the model achieved an AUC of 0.703 in external validation.The interpretable SHAP algorithm revealed that the serum calcium ion level was the crucial factor influencing the predictions of the model.CONCLUSION A superior predictive model,leveraging clinical data,has been crafted by employing the most effective XGBoost from a selection of ten algorithms.This model,by predicting the occurrence of AL in patients after rectal cancer resection,has identified the significant role of serum calcium ion levels,providing guidance for clinical practice.The integration of SHAP provides a clear interpretation of the model's predictions.
文摘BACKGROUND Parastomal hernia(PSH)is a common and challenging complication following preventive ostomy in rectal cancer patients,lacking accurate tools for early risk prediction.AIM To explore the application of machine learning algorithms in predicting the occurrence of PSH in patients undergoing preventive ostomy after rectal cancer resection,providing valuable support for clinical decision-making.METHODS A retrospective analysis was conducted on the clinical data of 579 patients who underwent rectal cancer resection with preventive ostomy at Tongji Hospital,Huazhong University of Science and Technology,between January 2015 and June 2023.Various machine learning models were constructed and trained using preoperative and intraoperative clinical variables to assess their predictive performance for PSH risk.SHapley Additive exPlanations(SHAP)were used to analyze the importance of features in the models.RESULTS A total of 579 patients were included,with 31(5.3%)developing PSH.Among the machine learning models,the random forest(RF)model showed the best performance.In the test set,the RF model achieved an area under the curve of 0.900,sensitivity of 0.900,and specificity of 0.725.SHAP analysis revealed that tumor distance from the anal verge,body mass index,and preoperative hypertension were the key factors influencing the occurrence of PSH.CONCLUSION Machine learning,particularly the RF model,demonstrates high accuracy and reliability in predicting PSH after preventive ostomy in rectal cancer patients.This technology supports personalized risk assessment and postoperative management,showing significant potential for clinical application.An online predictive platform based on the RF model(https://yangsu2023.shinyapps.io/parastomal_hernia/)has been developed to assist in early screening and intervention for high-risk patients,further enhancing postoperative management and improving patients’quality of life.
