Preoperative prediction of clinically relevant postoperative pancreatic fistula after pancreaticoduodenectomy. Issue 139 (June 2021)

• Record Type: Journal Article
• Authors: Lin, Ziying; Tang, Bingjun; Cai, Jinxiu; Wang, Xiangpeng; Li, Changxin; Tian, Xiaodong; Yang, Yinmo; Wang, Xiaoying
• Is Part Of: European journal of radiology. Issue 139(2021)
• Publication Date: 2021-06
• Subjects: PD pancreaticoduodenectomy -- POPF postoperative pancreatic fistula -- ISGPS International study group on pancreatic surgery -- BL biochemical leak -- CR-POPF clinically relevant postoperative pancreatic fistula -- FRS fistula risk score -- BMI body mass index -- PACS picture archiving and communication system -- VOI volume of interest -- DCA decision curve analysis -- Rad-score radiomics score -- cRad-score radiomics score of the combined model -- PCC Pearson correlation coefficient -- MPD main pancreatic duct; Radiomics -- Clinically relevant postoperative pancreatic fistula -- Prediction; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109693
• Languages: English
• ISSNs: 0720-048X

Abstract

Abstract: Objectives: To develop a radiomics model and a combined model for preoperative prediction of clinically relevant postoperative pancreatic fistula (CR-POPF) in patients undergoing pancreaticoduodenectomy and to compare the predictive performance of the two models with the traditional Fistula Risk Score system. Methods: A total of 250 patients who underwent pancreaticoduodenectomy (PD) with preoperative computed tomography (CT) were divided into a training set (n = 175) and validation set (n = 75). The pancreatic area was automatically segmented on the portal venous phase CT images using a 3D U-Net segmentation model. A radiomics model was developed using radiomics features extracted from the volume of interest (VOI) and a combined model was developed using radiomics features, demographic information and radiological features. The FRS was also used to predict POPF. The predictive performance of the prediction models was assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). Results: Eleven and 18 features were extracted for the radiomics model and combined model, respectively. The combined model showed excellent predictive value, with an AUC of 0.871 (95 %CI 0.816, 0.926) and 0.869 (95 %CI 0.779, 0.958) in the training cohort and validation cohort, respectively. Calibration curves and DCA showed that the combined model outperformed the traditional FRS system and radiomics model. Conclusion: The combined model exhibited excellent predictive performance and outperformed the traditional FRS system and radiomics model in the preoperative prediction of CR-POPF.