Zhang Xudong, Wang Jincheng, Wu Baoqiang, Li Tao, Jin Lei, Wu Yong, Gao Peng, Zhang Zhen, Qin Xihu, Zhu Chunfu
Department of Hepato-biliary-pancreatic Surgery, The Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University, Changzhou, Jiangsu, China.
Nanjing Medical University, Nanjing, Jiangsu, China.
J Clin Transl Hepatol. 2022 Apr 28;10(2):263-272. doi: 10.14218/JCTH.2021.00078. Epub 2021 Jun 30.
Gallbladder polyp (GBP) assessment aims to identify the early stages of gallbladder carcinoma. Many studies have analyzed the risk factors for malignant GBPs. In this retrospective study, we aimed to establish a more accurate predictive model for potential neoplastic polyps in patients with GBPs.
We developed a nomogram-based model in a training cohort of 233 GBP patients. Clinical information, ultrasonographic findings, and blood test findings were analyzed. Mann-Whitney U test and multivariate logistic regression analyses were used to identify independent predictors and establish the nomogram model. An internal validation was conducted in 225 consecutive patients. Performance and clinical benefit of the model were evaluated using receiver operating characteristic curves and decision curve analysis (DCA), respectively.
Age, cholelithiasis, carcinoembryonic antigen, polyp size, and sessile shape were confirmed as independent predictors of GBP neoplastic potential in the training group. Compared with five other proposed prediction methods, the established nomogram model presented better discrimination of neoplastic GBPs in the training cohort (area under the curve [AUC]: 0.846) and the validation cohort (AUC: 0.835). DCA demonstrated that the greatest clinical benefit was provided by the nomogram compared with the other five methods.
Our developed preoperative nomogram model can successfully be used to evaluate the neoplastic potential of GBPs based on simple clinical variables that maybe useful for clinical decision-making.
胆囊息肉(GBP)评估旨在识别胆囊癌的早期阶段。许多研究分析了恶性GBP的危险因素。在这项回顾性研究中,我们旨在为GBP患者潜在的肿瘤性息肉建立一个更准确的预测模型。
我们在一个由233例GBP患者组成的训练队列中开发了一种基于列线图的模型。分析了临床信息、超声检查结果和血液检查结果。采用Mann-Whitney U检验和多因素逻辑回归分析来识别独立预测因素并建立列线图模型。对225例连续患者进行了内部验证。分别使用受试者工作特征曲线和决策曲线分析(DCA)评估模型的性能和临床获益。
年龄、胆石症、癌胚抗原、息肉大小和无蒂形态被确认为训练组中GBP肿瘤性潜能的独立预测因素。与其他五种提出的预测方法相比,所建立的列线图模型在训练队列(曲线下面积[AUC]:0.846)和验证队列(AUC:0.835)中对肿瘤性GBP的区分能力更好。DCA表明,与其他五种方法相比,列线图提供的临床获益最大。
我们开发的术前列线图模型可以成功地用于基于简单临床变量评估GBP的肿瘤性潜能,这可能有助于临床决策。