Usefulness of artificial intelligence for predicting recurrence following surgery for pancreatic cancer: Retrospective cohort study

INTERNATIONAL JOURNAL OF SURGERY

卷:93

Article

Lee, Kwang-Sig¹  Jang, Jin-Young²  Yu, Young-Dong³  Heo, Jin Seok⁴  Han, Ho-Seong⁵  Yoon, Yoo-Seok⁵  Kang, Chang Moo⁶  Hwang, Ho Kyoung⁶  Kang, Sunghwa⁷ 

[1] Korea Univ, Coll Med, Al Ctr, Seoul, South Korea

[2] Seoul Natl Univ, Seoul Natl Univ Hosp, Dept Surg, Coll Med, Seoul, South Korea

[3] Korea Univ, Coll Med, Div HBP Surg & Liver Transplantat, Dept Surg, Seoul, South Korea

[4] Sungkyunkwan Univ, Samsung Med Ctr, Dept Surg, Div Hepatobiliary Pancreat Surg,Sch Med, 81 Irwon Ro, Seoul 06351, South Korea

[5] Seoul Natl Univ, Coll Med, Dept Surg, Bundang Hosp, Seoul, South Korea

[6] Yonsei Univ, Dept Surg, Div Hepatobiliary Pancreat Surg, Severance Hosp,Coll Med, Seoul, South Korea

[7] Dong A Univ Hosp, Div Hepatobiliary Pancreat Surg, Dept Surg, Pusan, South Korea

关键词: Pancreatic cancer;    Recurrence;    Artificial intelligence;   

DOI  :  10.1016/j.ijsu.2021.106050

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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