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Cited 17 time in webofscience Cited 18 time in scopus
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Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study

Authors
Lee, Kwang-SigJang, Jin-YoungYu, Young-DongHeo, Jin SeokHan, Ho-SeongYoon, Yoo-SeokKang, Chang MooHwang, Ho KyoungKang, Sunghwa
Issue Date
Sep-2021
Publisher
Surgical Association
Keywords
Pancreatic cancer; Recurrence; Artificial intelligence
Citation
International Journal of Surgery, v.93
Indexed
SCIE
SCOPUS
Journal Title
International Journal of Surgery
Volume
93
URI
https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/54395
DOI
10.1016/j.ijsu.2021.106050
ISSN
1743-9191
1743-9159
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 CIndex 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 < 0.05). The C-Index averages of the random forest and the Cox model were 0.6805 and 0.7738, respectively. 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.
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2. Clinical Science > Department of Hepato-Biliary-Pancreatic Surgery > 1. Journal Articles
4. Research institute > Institute of Human Behavior and Genetics > 1. Journal Articles

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