Copyright
©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence
In Woong Han, Kyeongwon Cho, Youngju Ryu, Sang Hyun Shin, Jin Seok Heo, Dong Wook Choi, Myung Jin Chung, Oh Chul Kwon, Baek Hwan Cho
In Woong Han, Youngju Ryu, Sang Hyun Shin, Jin Seok Heo, Dong Wook Choi, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
Kyeongwon Cho, Myung Jin Chung, Baek Hwan Cho, Medical Artificial Intelligence Research Center, Department of Medical Device Management and Research, SAIHST, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
Myung Jin Chung, Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
Oh Chul Kwon, Artificial Intelligence Research Center, Medical DataBase Incorporated, Seoul 06048, South Korea
Author contributions: Han IW and Cho K contributed equally to this work; Han IW and Cho BH designed the research the paper; Han IW, Cho K, and Kwon OC performed the research and wrote the paper; Ryu Y, Shin SH, Heo JS, and Choi DW contributed to the analysis and provided clinical advice; Chung MJ and Cho BH supervised the report.
Supported by the National Research Foundation of Korea grant funded by the Korea government (Ministry of Science and ICT), No. NRF-2019R1F1A1042156; and the Bio & Medical Technology Development Program, No. NRF-2017M3A9E1064784.
Institutional review board statement: This study was reviewed and approved by Institutional review board of Samsung Medical Center (number: SMC 2017-01-017).
Informed consent statement: Patients were not required to give informed consent to this retrospective study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: There are no financial or any potential personal conflicts of interest to declare for any of the authors.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Baek Hwan Cho, PhD, Assistant Professor, Medical AI Research Center, Department of Medical Device Management and Research, SAIHST, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, South Korea. baekhwan.cho@samsung.com
Received: April 22, 2020
Peer-review started: April 22, 2020
First decision: April 29, 2020
Revised: July 13, 2020
Accepted: July 30, 2020
Article in press: July 30, 2020
Published online: August 14, 2020
ARTICLE HIGHLIGHTS
Research background
Despite advancements in operative technique and improvements in postoperative managements, postoperative pancreatic fistula (POPF) is a life-threatening complication following pancreatoduodenectomy (PD). Artificial intelligence (AI) technology is being actively used in the medical field, but few studies have reported applying it to outcomes after PD.
Research motivation
There are some reports to predict POPF preoperatively or intraoperatively, but the accuracy of those is questionable. Compared with established POPF risk prediction methods, we expect that our ML algorithms can better predict the POPF risk correctly.
Research objectives
This study aimed to develop a risk prediction platform for POPF with single center dataset using an AI model.
Research methods
A total of 38 variables from 1769 patients who underwent PD from 2007 to 2016 at Samsung Medical Center were inserted into AI-driven algorithms. The algorithms tested to make the risk prediction platform were random forest (RF) and a neural network (NN) with or without recursive feature elimination (RFE). These algorithms can better incorporate multiple risk factors and account for more nuanced relationships between the risk factors and POPF. The median imputation method was used for missing values.
Research results
The number of POPFs was 221 (12.5%) according to the International Study Group of Pancreatic Fistula definition 2016. The maximal AUC using NN with RFE was 0.74. Sixteen risk factors for POPF were identified by AI algorithm: Pancreatic duct diameter, body mass index, preoperative serum albumin, lipase level, amount of intraoperative fluid infusion, age, platelet count, extrapancreatic location of tumor, combined venous resection, co-existing pancreatitis, neoadjuvant radiotherapy, American Society of Anesthesiologists’ score, sex, soft texture of the pancreas, underlying heart disease, and preoperative endoscopic biliary decompression. We developed a web-based POPF prediction platform available at https://popfrisk.smchbp.org.
Research conclusions
This study is the first to predict POPF with multiple risk factors using AI. This platform is reliable (AUC 0.74), so it could be used to select patients who need especially intense therapy and to preoperatively establish an effective treatment strategy.
Research perspectives
This study developed a risk prediction platform for POPF with single center dataset using an AI model. The follow-up study will be conducted by performing an external validation on patients in multiple institutions. Also, future prospective studies could stratify treatments based on the outcome of this platform and provide comprehensive treatment algorithms.
