Machine learning for modeling and identifying risk factors of pancreatic fistula  

作　　者：Mikhail B Potievskiy Leonid O Petrov Sergei A Ivanov Pavel V Sokolov Vladimir S Trifanov Nikolai A Grishin Ruslan I Moshurov Peter V Shegai Andrei D Kaprin 

机构地区：[1]Center for Clinical Trials,Center for Innovative Radiological and Regenerative Technologies,FSBI“National Medical Research Radiological Center”of the Ministry of Health of the Russian Federation,Obninsk 249036,Kaluzhskaya Oblast,Russia [2]Department of Radiation and Surgical Treatment of Abdominal Diseases,A.Tsyb Medical Radiological Center,FSBI“National Medical Research Radiological Center”of the Ministry of Health of the Russian Federation,Obninsk 249036,Kaluzhskaya Oblast,Russia [3]Department of Administration,FSBI“National Medical Research Radiological Center”of the Ministry of Health of the Russian Federation,Obninsk 249036,Kaluzhskaya Oblast,Russia [4]Department of Operation Unit,FSBI“National Medical Research Radiological Center”of the Ministry of Health of the Russian Federation,Obninsk 249036,Kaluzhskaya Oblast,Russia [5]Department of Abdominal Oncology,P.Herzen Moscow Oncological Institute,FSBI“National Medical Research Radiological Center”of the Ministry of Health of the Russian Federation,Obninsk 249036,Kaluzhskaya Oblast,Russia [6]Center for Innovative Radiological and Regenerative Technologies,FSBI“National Medical Research Radiological Center”of the Ministry of Health of the Russian Federation,Obninsk 249036,Kaluzhskaya Oblast,Russia [7]Department of Urology and Operative Nephrology with Course of Oncology,Medical Faculty,Medical Institute,Peoples’Friendship University of Russia,Moscow 117198,Moskva,Russia

出　　处：《World Journal of Gastrointestinal Oncology》2025年第4期104-115,共12页世界胃肠肿瘤学杂志(英文)

摘　　要：BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions,including bleeding due to visceral vessel erosion and peritonitis.AIM To develop a machine learning(ML)model for postoperative pancreatic fistula and identify significant risk factors of the complication.METHODS A single-center retrospective clinical study was conducted which included 150 patients,who underwent pancreat-oduodenectomy.Logistic regression,random forest,and CatBoost were employed for modeling the biochemical leak(symptomless fistula)and fistula grade B/C(clinically significant complication).The performance was estimated by receiver operating characteristic(ROC)area under the curve(AUC)after 5-fold cross-validation(20%testing and 80%training data).The risk factors were evaluated with the most accurate algorithm,based on the parameter“Importance”(Im),and Kendall correlation,P<0.05.RESULTS The CatBoost algorithm was the most accurate with an AUC of 74%-86%.The study provided results of ML-based modeling and algorithm selection for pancreatic fistula prediction and risk factor evaluation.From 14 parameters we selected the main pre-and intraoperative prognostic factors of all the fistulas:Tumor vascular invasion(Im=24.8%),age(Im=18.6%),and body mass index(Im=16.4%),AUC=74%.The ML model showed that biochemical leak,blood and drain amylase level(Im=21.6%and 16.4%),and blood leukocytes(Im=11.2%)were crucial predictors for subsequent fistula B/C,AUC=86%.Surgical techniques,morphology,and pancreatic duct diameter less than 3 mm were insignificant(Im<5%and no correlations detected).The results were confirmed by correlation analysis.CONCLUSION This study highlights the key predictors of postoperative pancreatic fistula and establishes a robust ML-based model for individualized risk prediction.These findings contribute to the advancement of personalized periop-erative care and may guide targeted preventive strategies.

关 键 词：PANCREATODUODENECTOMY Postoperative pancreatic fistula Risk factors Machine learning Precision oncology 

分 类 号：R57[医药卫生—消化系统]