Zhu Pengpeng, Liao Yan, Fan Jiyuan, Li Xin, Su Lili, Li Jun, Yuan Shengguang, Yu Junxiong, Liao Weijia
Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, P.R. China.
Disease Prevention and Control Center of Guilin, Guilin, Guangxi, P.R. China.
Oncotarget. 2017 Oct 31;8(61):104227-104237. doi: 10.18632/oncotarget.22198. eCollection 2017 Nov 28.
Hepatocellular carcinoma (HCC) has a high predilection with portal vein tumor thrombosis (PVTT). However, part of the PVTT type can be found only under the microscopy, which was namely as type I. The objective of this study was to establish a simple and inexpensive non-invasive model to predict the presentation of PVTT at HCC patients. A total of 815 HCC patients were retrospectively evaluated and randomly assigned into 2 groups: the training group (n = 408) and validation group (n = 407). A new index model, namely WγAL, was built to predict the presence of PVTT in the training subjects and was further validated in the validation subjects. At the optimal cutoff of 8.90, WγAL identified patients with a hazard ratio (HR) of 7.139 for the presence of PVTT. The area under receiver operating characteristic (AUROC) of WγAL was 0.795 (sensitivity: 71.9%; specificity: 78.6%) for differentiation between PVTT and non-PVTT patients in the training group. The AUROC of WγAL in differentiating patients with PVTT type I from non-PVTT patients was 0.748 (sensitivity: 72.1%; specificity: 68.4%) with an HR of 5.355. In addition, WγAL > 8.90 was significantly associated with large tumors, multiple tumor numbers, TNM stage III-IV, metastasis, and overall survival in HCC patients. The novel predictive model represents a simple and inexpensive model that can identify the presence of PVTT in HCC patients with a high degree of accuracy, with important clinical significance in the future therapeutic management of HCC patients.
肝细胞癌(HCC)极易发生门静脉癌栓(PVTT)。然而,部分PVTT类型仅在显微镜下才能发现,即I型。本研究的目的是建立一种简单且低成本的非侵入性模型,以预测HCC患者PVTT情况。对815例HCC患者进行回顾性评估,并随机分为两组:训练组(n = 408)和验证组(n = 407)。构建了一个新的指标模型,即WγAL,用于预测训练组患者中PVTT的存在情况,并在验证组中进一步验证。在最佳临界值8.90时,WγAL识别出PVTT存在的风险比(HR)为7.139的患者。在训练组中,WγAL区分PVTT和非PVTT患者的受试者操作特征曲线下面积(AUROC)为0.795(敏感性:71.9%;特异性:78.6%)。WγAL区分I型PVTT患者和非PVTT患者的AUROC为0.748(敏感性:72.1%;特异性:68.4%),HR为5.355。此外,WγAL > 8.90与HCC患者的大肿瘤、多个肿瘤数量、TNM III-IV期、转移及总生存期显著相关。该新型预测模型是一种简单且低成本的模型,能够高度准确地识别HCC患者中PVTT的存在情况,对未来HCC患者的治疗管理具有重要临床意义。