Zheng Chongming, Chen Xiaotian, Zhang Zhewei, Li Anlvna, Wang Junwei, Cai Tingting, Tang Yanping, An Xuewen, Lu Fei, Chen Gang, Xiang Youqun
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Wenzhou Medical University, Wenzhou, China.
Transl Cancer Res. 2022 Jul;11(7):1898-1908. doi: 10.21037/tcr-22-123.
To investigate the predictive value of intramuscular adipose tissue content (IMAC) on the outcome of gallbladder cancer (GBC) patients after resection, by then develop and evaluate a nomogram to predict the prognosis of GBC patients.
This research incorporated 123 patients with a pathological diagnosis of GBC. Evaluating the prognosis by the Kaplan-Meier method. Independent predictors of overall survival (OS) were screened using multifactorial Cox regression analysis, and a nomogram was constructed from these. Consistency index and calibration curve were used to identify and calibrate the nomogram. The accuracy of the nomogram was assessed by receiver operating characteristic (ROC) curve and decision curve analysis (DCA) was used to assess the net benefit.
Patients with high IMAC showed a worse prognosis. A nomogram was constructed to predict OS based on IMAC. The C-index for the nomogram was 0.804. The calibration curve showed well performance of the nomogram. The area under the ROC curve (AUC) for the nomogram at three and five years was 0.839 and 0.785, respectively. A high net benefit was demonstrated by DCA.
IMAC was a valid predictor for GBC patients. A nomogram with good performance is constructed to predict the prognosis of GBC patients.
探讨肌肉内脂肪组织含量（IMAC）对胆囊癌（GBC）患者切除术后预后的预测价值，并开发和评估一种预测GBC患者预后的列线图。
本研究纳入123例经病理诊断为GBC的患者。采用Kaplan-Meier法评估预后。使用多因素Cox回归分析筛选总生存（OS）的独立预测因素，并据此构建列线图。使用一致性指数和校准曲线来识别和校准列线图。通过受试者工作特征（ROC）曲线评估列线图的准确性，并使用决策曲线分析（DCA）评估净效益。
IMAC高的患者预后较差。构建了基于IMAC预测OS的列线图。该列线图的C指数为0.804。校准曲线显示列线图性能良好。列线图在3年和5年时的ROC曲线下面积（AUC）分别为0.839和0.785。DCA显示出较高的净效益。
IMAC是GBC患者的有效预测指标。构建了性能良好的列线图来预测GBC患者的预后。
