Shen Junyi, Wen Tianfu, Chen Weixia, Lu Changli, Yan Lvnan, Yang Jiayin
Department of Liver Surgery and Liver Transplantation Center, West China Hospital, Sichuan University, Chengdu, China.
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
ANZ J Surg. 2018 Nov;88(11):E761-E766. doi: 10.1111/ans.14473. Epub 2018 Apr 24.
Microvascluar invasion and satellite lesion (MS), important unfavourable pathological factors, significantly contribute to tumour recurrence and impair the prognosis in hepatocellular carcinoma. We aimed to construct a model for the prediction of MS in order to plan treatment better.
A total of 1135 consecutive patients with hepatocellular carcinoma who received radical hepatectomy at West China Hospital were randomly assigned to a training set and a validation set. Multivariate analysis was preformed to identify independent risk factors of MS in the training set, and a nomogram was then constructed based on the risk factors. The concordance index (C-index) and a calibration curve were used to assess the predictive performance of the model.
The occurrence rate of MS was about 36.5%. Based on the multivariate analysis, the following six variables were incorporated into the nomogram: age (hazard ratio (HR): 0.531), alpha fetoprotein (HR: 1.327), neutrophil-to-lymphocyte ratio (>2.8, HR: 1.732), international normalized ratio (>1.07, HR: 1.702), tumour size (HR: 1.116) and tumour number (HR: 1.842). The model showed satisfactory discrimination abilities, with a C-index of 0.721 for the training set and 0.704 for the validation set. The receiver operating characteristic curve confirmed the predictive power. Meanwhile, the calibration curve presented a goodness of fit between prediction of the model and actual observations.
The user-friendly model may be useful for prediction of the occurrence of MS and to plan treatment more rationally preoperatively.
微血管侵犯和卫星灶(MS)是重要的不良病理因素,对肝细胞癌的肿瘤复发有显著影响,并损害其预后。我们旨在构建一个预测MS的模型,以便更好地规划治疗方案。
共有1135例在华西医院接受根治性肝切除术的连续肝细胞癌患者被随机分配到训练集和验证集。在训练集中进行多因素分析以确定MS的独立危险因素,然后基于这些危险因素构建列线图。一致性指数(C指数)和校准曲线用于评估模型的预测性能。
MS的发生率约为36.5%。基于多因素分析,以下六个变量被纳入列线图:年龄(风险比(HR):0.531)、甲胎蛋白(HR:1.327)、中性粒细胞与淋巴细胞比值(>2.8,HR:1.732)、国际标准化比值(>1.07,HR:1.702)、肿瘤大小(HR:1.116)和肿瘤数量(HR:1.842)。该模型显示出令人满意的区分能力,训练集的C指数为0.721,验证集的C指数为0.704。受试者工作特征曲线证实了其预测能力。同时,校准曲线显示模型预测与实际观察之间具有良好的拟合度。
这个用户友好型模型可能有助于预测MS的发生,并在术前更合理地规划治疗方案。