Online calculator for predicting the risk of malignancy in patients with pancreatic cystic neoplasms: A multicenter, retrospective study

用于预测胰腺囊性肿瘤患者恶性肿瘤风险的在线计算器:一项多中心回顾性研究

阅读:1

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.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。