Jiang D, Chen ZX, Ma FX, Gong YY, Pu T, Chen JM, Liu XQ, Zhao YJ, Xie K, Hou H, Wang C, Geng XP, Liu FB. Online calculator for predicting the risk of malignancy in patients with pancreatic cystic neoplasms: A multicenter, retrospective study. World J Gastroenterol 2022; 28(37): 5469-5482 [PMID: 36312834 DOI: 10.3748/wjg.v28.i37.5469]
Corresponding Author of This Article
Fu-Bao Liu, MD, PhD, Chief Doctor, Professor, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, No. 81 Meishan Road, Hefei 230000, Anhui Province, China. lancetlfb@126.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
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/
Dong Jiang, Zi-Xiang Chen, Fu-Xiao Ma, Yu-Yong Gong, Tian Pu, Jiang-Ming Chen, Xue-Qian Liu, Yi-Jun Zhao, Kun Xie, Xiao-Ping Geng, Fu-Bao Liu, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
Hui Hou, Department of General Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei 230000, Anhui Province, China
Cheng Wang, Department of General Surgery, The First Affiliated Hospital of University of Science and Technology of China, Hefei 230000, Anhui Province, China
Author contributions: Jiang D, Chen JM, Chen ZX and Ma FX contributed to the data analysis and participated in drafting the article; Liu XQ, Gong YY, and Pu T extracted the clinical data and calculated the clinical correlations; Xie K and Zhao YJ interpreted the results and revised the manuscript; Liu FB, Hou H, Wang C and Geng XP gave final approval of the version to be published; All authors contributed to the design and interpretation of the study and to further drafts and approved the final version to be published.
Supported byUniversity Natural Science Research Project of Anhui Province, No. KJ2021ZD0021.
Institutional review board statement: The study was reviewed and approved by the Institutional ethics committees of the First Affiliated Hospital of Anhui Medical University (Approval Quick-PJ2022-06-26).
Informed consent statement: All study participants or their legal guardian provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Fu-Bao Liu, MD, PhD, Chief Doctor, Professor, Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, No. 81 Meishan Road, Hefei 230000, Anhui Province, China. lancetlfb@126.com
Received: June 16, 2022 Peer-review started: June 16, 2022 First decision: July 12, 2022 Revised: July 25, 2022 Accepted: September 8, 2022 Article in press: September 8, 2022 Published online: October 7, 2022 Processing time: 105 Days and 7.7 Hours
Abstract
BACKGROUND
Efficient and practical methods for predicting the risk of malignancy in patients with pancreatic cystic neoplasms (PCNs) are lacking.
AIM
To establish a nomogram-based online calculator for predicting the risk of malignancy in patients with PCNs.
METHODS
In this study, the clinicopathological data of target patients in three medical centers were analyzed. The independent sample t-test, Mann–Whitney U test or chi-squared test were used as appropriate for statistical analysis. After univariable and multivariable logistic regression analysis, five independent factors were screened and incorporated to develop a calculator for predicting the risk of malignancy. Finally, the concordance index (C-index), calibration, area under the curve, decision curve analysis and clinical impact curves were used to evaluate the performance of the calculator.
RESULTS
Enhanced mural nodules [odds ratio (OR): 4.314; 95% confidence interval (CI): 1.618–11.503, P = 0.003], tumor diameter ≥ 40 mm (OR: 3.514; 95%CI: 1.138–10.849, P = 0.029), main pancreatic duct dilatation (OR: 3.267; 95%CI: 1.230–8.678, P = 0.018), preoperative neutrophil-to-lymphocyte ratio ≥ 2.288 (OR: 2.702; 95%CI: 1.008–7.244, P = 0.048], and preoperative serum CA19-9 concentration ≥ 34 U/mL (OR: 3.267; 95%CI: 1.274–13.007, P = 0.018) were independent risk factors for a high risk of malignancy in patients with PCNs. In the training cohort, the nomogram achieved a C-index of 0.824 for predicting the risk of malignancy. The predictive ability of the model was then validated in an external cohort (C-index: 0.893). Compared with the risk factors identified in the relevant guidelines, the current model showed better predictive performance and clinical utility.
CONCLUSION
The calculator demonstrates optimal predictive performance for identifying the risk of malignancy, potentially yielding a personalized method for patient selection and decision-making in clinical practice.
Core Tip: A nomogram-based online calculator for predicting the risk of malignancy in patients with pancreatic cystic neoplasms was developed. The calculator demonstrates optimal predictive performance for identifying the risk of malignancy, potentially yielding a personalized method for patient selection and decision-making in clinical practice.
