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肝细胞癌合并门静脉癌栓危险因素分析及诊断模型构建

Analysis of risk factors for hepatocellular carcinoma complicated with portal vein tumor thrombus and construction of diagnostic model

投稿时间：2024-09-16 修订日期：2024-11-10

DOI：

中文关键词: 肝细胞癌门静脉癌栓影响因素诊断

英文关键词:Hepatocellular carcinoma Portal vein tumor thrombus Influencing factors diagnosis

基金项目:广西自然科学(2018GXNSFAA281126)

作者	单位	邮编
吴伟珍	广西医科大学第一附属医院肿瘤内科广西南宁	530021
戚茂建	广西医科大学第一附属医院肿瘤内科广西南宁
唐海军	广西医科大学第一附属医院脊柱骨病外科广西南宁
李河柠	广西医科大学第一附属医院肿瘤内科广西南宁
马劼^*	广西医科大学第一附属医院肿瘤内科广西南宁广西医科大学第一附属医院脊柱骨病外科广西南宁	530021

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中文摘要:

[目的] 探讨影响肝细胞癌合并门静脉癌栓的危险因素并构建诊断模型。[方法] 回顾性收集2021年7月至2023年11月期间广西医科大学第一附属医院肿瘤内科收治的84例肝细胞癌患者的临床资料，根据腹部CT、MRI或术后病理结果是否有门静脉癌栓形成分为癌栓组（44例）和非癌栓组（40例）。比较两组临床资料，Logistic逐步回归法分析肝细胞癌合并门静脉癌栓的影响因素，构建列线图（Nomogram）模型，绘制校准曲线评估模型校准度，绘制受试者工作特征（Receiver operating characteristic，ROC）曲线评估诊断效能，绘制决策曲线、临床影响曲线评估临床实用性。[结果] Logistic回归分析显示，肿瘤远处转移（OR=4.885，95%CI：1.632~14.620，P=0.005）、异常凝血酶原（Protein induced by vitamin K absence or antagonist-Ⅱ，PIVKA-Ⅱ）（OR=2.805，95%CI：1.052~7.476，P=0.039）、D-二聚体（D-dimer，D-D）（OR=3.059，95%CI：1.313~7.127，P=0.010）与肝细胞癌合并门静脉癌栓的相关。构建Nomogram模型，经验证校准曲线接近理想曲线，ROC曲线下面积为0.818（95%CI：0.730~0.906，P<0.001），决策曲线和临床影响曲线显示该模型具有较好的临床实用性。[结论] 肿瘤远处转移、血清PIVKA-Ⅱ、血浆D-D与门静脉癌栓的发生相关，基于三者构建的诊断模型在肝细胞癌合并门静脉癌栓的早期诊断具有一定的临床意义和参考价值。

英文摘要:

[Objective] To identify risk factors for hepatocellular carcinoma complicated by portal vein tumor thrombus (PVTT) and to develop a diagnostic model. [Methods] Clinical data from 84 patients with hepatocellular carcinoma, who received treatment at the Department of Oncology, First Affiliated Hospital of Guangxi Medical University between July 2021 and November 2023, were retrospectively collected. Based on the presence of PVTT, confirmed by abdominal CT, MRI, or postoperative pathological evaluation, the patients were categorized into a thrombus group (n = 44) and a non-thrombus group (n = 40). Comparative analysis of clinical data between the two groups was performed. Stepwise logistic regression was utilized to identify the influencing factors associated with hepatocellular carcinoma complicated by PVTT. A nomogram model was subsequently developed, and a calibration curve was constructed to evaluate the model’s calibration accuracy. The diagnostic performance was assessed using the receiver operating characteristic (ROC) curve. Decision curve analysis and clinical impact curves were used to evaluate the clinical applicability and utility of the model. [Results] Logistic regression analysis identified distant metastasis (OR = 4.885, 95% CI: 1.632~14.620, P = 0.005), elevated prothrombin levels (protein induced by vitamin K absence or antagonist-II, PIVKA-II; OR = 2.805, 95% CI: 1.052~7.476, P = 0.039), and increased D-dimer (D-D) levels (OR = 3.059, 95% CI: 1.313~7.127, P = 0.010) as factors associated with hepatocellular carcinoma complicated by PVTT. A nomogram model was developed, and the calibration curve demonstrated good agreement with the ideal reference line. The area under the ROC curve (AUC) was 0.818 (95% CI: 0.730~0.906, P < 0.001), indicating strong discriminative ability. Decision curve analysis and the clinical impact curve further confirmed the favorable clinical utility of the model. [Conclusion] Distant metastasis, serum PIVKA-II, and plasma D-D levels were found to be significantly associated with the development of PVTT. The diagnostic model constructed based on these three factors demonstrated promising clinical utility and may serve as a valuable tool for the early diagnosis of hepatocellular carcinoma complicated by PVTT.

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