Copyright
©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Nov 15, 2019; 11(11): 1043-1053
Published online Nov 15, 2019. doi: 10.4251/wjgo.v11.i11.1043
Published online Nov 15, 2019. doi: 10.4251/wjgo.v11.i11.1043
Validation and head-to-head comparison of four models for predicting malignancy of intraductal papillary mucinous neoplasm of the pancreas: A study based on endoscopic ultrasound findings
Jie Hua, Bo Zhang, Yi-Yin Zhang, Miao-Yan Wei, Chen Liang, Qing-Cai Meng, Jiang Liu, Xian-Jun Yu, Jin Xu, Si Shi, Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Fudan University Shanghai Medical College, Shanghai Pancreatic Cancer Institute, Shanghai 200032, China
Xiu-Jiang Yang, Department of Endoscopy, Fudan University Shanghai Cancer Center, Shanghai 200032, China
Author contributions: Hua J, Yang XJ, Yu XJ, and Shi S conceived and designed the study; Hua J, Zhang YY, Wei MY, Liang C, Meng QC, and Liu J acquired and analyzed the data; Hua J, Zhang B, Zhang YY, and Liang C performed statistical analysis; Hua J, Zhang B, Wei MY, and Meng QC drafted the manuscript; Yang XJ, Yu XJ, Xu J, and Shi S critically revised the manuscript; Yu XJ, Xu J, and Shi S provided funding support; Yang XJ, Yu XJ, Xu J, and Shi S supervised the study.
Supported by The China National Funds for Distinguished Young Scientists , No. 81625016 ; The National Natural Science Foundation of China , No. 81772555 ; The Science and Technology Commission of Shanghai Municipality , No. 17YF1402500 .
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Fudan University Shanghai Cancer Center.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All authors declare no conflicts of interest related to this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: Si Shi, MD, Attending Doctor, Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270, Dong’an Road, Xuhui District, Shanghai 200032, China. shisi@fudanpci.org
Telephone: +86-21-64175590 Fax: +86-21-64031446
Received: May 22, 2019
Peer-review started: May 23, 2019
First decision: July 31, 2019
Revised: September 6, 2019
Accepted: September 13, 2019
Article in press: September 13, 2019
Published online: November 15, 2019
Processing time: 177 Days and 15.5 Hours
Peer-review started: May 23, 2019
First decision: July 31, 2019
Revised: September 6, 2019
Accepted: September 13, 2019
Article in press: September 13, 2019
Published online: November 15, 2019
Processing time: 177 Days and 15.5 Hours
Core Tip
Core tip: There are currently four available models for predicting the malignancy of intraductal papillary mucinous neoplasm (IPMN). Whether one model is superior to other models and whether it can be widely applied in clinical practice remain unknown. To address this knowledge gap, we performed a head-to-head comparison of the four models for predicting the probability of malignancy in IPMN patients. The results suggest that the model reported by the Pancreatic Surgery Consortium (PSC model) exhibited the best performance characteristics. Therefore, we believe that the PSC model should be considered the best tool for the individualized prediction of malignancy in patients with IPMN, which can facilitate clinical decision-making.