Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence

Figure 1 Performance of the neural network models optimized within each recursive feature elimination step. 1: Pancreatic duct diameter; 2: Body mass index; 3: Serum albumin; 4: Amount of intraoperative fluid infusion; 5: Age; 6: Platelet count; 7: Extrapancreatic location of tumor; 8: Combined venous resection; 9: Co-existing pancreatitis; 10: Serum lipase; 11: Neoadjuvant radiotherapy; 12: ASA score; 13: Sex; 14: Soft texture of pancreas; 15: Underlying heart disease; 16: Preoperative endoscopic biliary decompression; 17: Hemoglobin; 18: Serum total bilirubin; 19: Operative time; 20: Intraoperative transfusion; 21: Neoadjuvant chemotherapy; 22: Anastomotic methods (1); 23: Serum amylase; 24: Anastomotic methods (2-1); 25: Pancreatic duct stent (1); 26: White blood cell count; 27: Type of surgery (1); 28: Serum carbohydrate antigen 19-9; 29: Serum C- reactive protein; 30 Estimated blood loss; 31: Combined vascular resection; 32: Pancreatic duct stent (2); 33: Preoperative percutaneous biliary drainage; 34: Underlying cerebrovascular disease; 35: Combined organ resection; 36: Type of surgery (2); 37: Type of surgery (3); 38: Anastomotic methods (2-2); 39: Underlying liver disease; 40: Underlying chronic kidney disease; 41: Underlying pulmonary disease; 42: Underlying cerebrovascular disease; 43: Diabetes mellitus; 44: Preoperative endoscopic pancreatic drainage; ASA: American Society of Anesthesiologists; AUC: Area under the curve.