Lee, Kwang-Sig; Jang, Jin-Young; Yu, Young-Dong; Heo, Jin Seok; Han, Ho-Seong; Yoon, Yoo-Seok; Kang, Chang Moo; Hwang, Ho Kyoung; Kang, Sunghwa
International journal of surgery (London, England)
2021Aug ; 93 ( 1 ) :106050.
PMID : 34388677
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Lee, Kwang-Sig - AI Center, Korea University College of Medicine, Seoul, South Korea.
Jang, Jin-Young - Department of Surgery, Seoul National University Hospital, Seoul National University
Yu, Young-Dong - Division of HBP Surgery & Liver Transplantation, Department of Surgery, Korea
Heo, Jin Seok - Division of Hepatobiliary-Pancreatic Surgery, Department of Surgery, Samsung Medical
Han, Ho-Seong - Department of Surgery, Seoul National University Bundang Hospital, Seoul National
Yoon, Yoo-Seok - Department of Surgery, Seoul National University Bundang Hospital, Seoul National
Kang, Chang Moo - Division of Hepatobiliary-Pancreatic Surgery, Department of Surgery, Severance
Hwang, Ho Kyoung - Division of Hepatobiliary-Pancreatic Surgery, Department of Surgery, Severance
Kang, Sunghwa - Division of Hepatobiliary-Pancreatic Surgery, Department of Surgery, Dong-A
ABSTRACT
BACKGROUND: or Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of mortality in the world with the overall 5-year survival rate of 6%. The survival of patients with PDAC is closely related to recurrence and therefore it is necessary to identify the risk factors for recurrence. This study uses artificial intelligence approaches and multi-center registry data to analyze the recurrence of pancreatic cancer after surgery and its major determinants.
METHODS: Data came from 4846 patients enrolled in a multi-center registry system, the Korea Tumor Registry System (KOTUS). The random forest and the Cox proportional-hazards model (the Cox model) were applied and compared for the prediction of disease-free survival. Variable importance, the contribution of a variable for the performance of the model, was used for identifying major predictors of disease-free survival after surgery. The C-Index was introduced as a criterion for validating the models trained.
RESULTS: Based on variable importance from the random forest, major predictors of disease-free survival after surgery were tumor size (0.00310), tumor grade (0.00211), TNM stage (0.00211), T stage (0.00146) and lymphovascular invasion (0.00125). The coefficients of these variables were statistically significant in the Cox model (p?
CONCLUSIONS: This is the first artificial-intelligence study with multi-center registry data to predict disease-free survival after the surgery of pancreatic cancer. The findings of this methodological study demonstrate that artificial intelligence can provide a valuable decision-support system for treating patients undergoing surgery for pancreatic cancer. However, at present, further studies are needed to demonstrate the actual benefit of applying machine learning algorithms in clinical practice. CI - Copyright ??2021 IJS Publishing Group Ltd. Published by Elsevier Ltd. All rights reserved.
keyword
Artificial intelligence; Pancreatic cancer; Recurrence
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