Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study. (September 2021)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: International journal of surgery. Volume 93(2021)
• Publication Date: 2021-09
• Subjects: Pancreatic cancer -- Recurrence -- Artificial intelligence; Surgery -- Periodicals; Surgical Procedures, Operative -- Periodicals; 617.005
• DOI: 10.1016/j.ijsu.2021.106050
• Languages: English
• ISSNs: 1743-9191

Abstract

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 Ko rea Tu mor Registry S ystem (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 < 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. Highlights: This study uses artificial intelligence to analyze the recurrence of pancreatic cancer after surgery and its major determinants. We retrospectively reviewed 4846 patients enrolled in a multi-center registry system. The performance of random forest was acceptable in predicting cancer recurrence. Artificial intelligence can provide a valuable decision-support system for treating patients for pancreatic cancer.