AutoFRS: an externally validated, annotation-free approach to computational preoperative complication risk stratification in pancreatic surgery - an experimental study.
Authors
Affiliations (11)
Affiliations (11)
- Department of Visceral, Thoracic and Vascular Surgery, University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
- National Center for Tumor Diseases (NCT/UCC), Dresden, Germany; German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany.
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana.
- Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, Indiana.
- Division of Translational Surgical Oncology, National Center for Tumor Diseases (NCT), Partner Site Dresden, Dresden, Germany.
- Faculty of Medicine and University Hospital Carl Gustav Carus Dresden, Technische Universität Dresden, Institute and Polyclinic for Diagnostic and Interventional Radiology, Dresden, Germany.
- Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, OncoRay - National Center for Radiation Research in Oncology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany.
- Cantonal Hospital Thurgau, Frauenfeld, Switzerland.
- Clarunis, University Digestive Health Care Center Basel, Switzerland.
- Technische Universität Dresden, Centre for Tactile Internet with Human-in-the-Loop (CeTI), Dresden, Germany.
Abstract
The risk of postoperative pancreatic fistula (POPF), one of the most dreaded complications after pancreatic surgery, can be predicted from preoperative imaging and tabular clinical routine data. However, existing studies suffer from limited clinical applicability due to a need for manual data annotation and a lack of external validation. We propose AutoFRS (automated fistula risk score software), an externally validated end-to-end prediction tool for POPF risk stratification based on multimodal preoperative data. We trained AutoFRS on preoperative contrast-enhanced computed tomography imaging and clinical data from 108 patients undergoing pancreatic head resection and validated it on an external cohort of 61 patients. Prediction performance was assessed using the area under the receiver operating characteristic curve (AUC) and balanced accuracy. In addition, model performance was compared to the updated alternative fistula risk score (ua-FRS), the current clinical gold standard method for intraoperative POPF risk stratification. AutoFRS achieved an AUC of 0.81 and a balanced accuracy of 0.72 in internal validation and an AUC of 0.79 and a balanced accuracy of 0.70 in external validation. In a patient subset with documented intraoperative POPF risk factors, AutoFRS (AUC: 0.84 ± 0.05) performed on par with the uaFRS (AUC: 0.85 ± 0.06). The AutoFRS web application facilitates annotation-free prediction of POPF from preoperative imaging and clinical data based on the AutoFRS prediction model. POPF can be predicted from multimodal clinical routine data without human data annotation, automating the risk prediction process. We provide additional evidence of the clinical feasibility of preoperative POPF risk stratification and introduce a software pipeline for future prospective evaluation.