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通讯作者:

吴志奇,E-mail:qiecho@126.com

中图分类号:R735.7

文献标识码:A

文章编号:1007-4368(2023)03-357-08

DOI:10.7655/NYDXBNS20230309

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参考文献 9
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参考文献 10
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参考文献 14
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参考文献 15
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参考文献 16
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参考文献 17
NATARPAOLO A,LAYESE R,MAGISTRI P,et al.Vali⁃ dation of the AFP model as a predictor of HCC recurrence in patients with viral hepatitis ⁃ related cirrhosis who had received a liver transplant for HCC[J].J Hepatol,2017,66(3):552-559
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HUANG C F,YEN M L,TSAI P C,et al.Baseline gam⁃ ma ⁃glutamyl transferase levels strongly correlate with he⁃ patocellular carcinoma development in non ⁃ cirrhotic pa⁃ tients with successful hepatitis C virus eradication[J].J Hepatol,2014,61(1):67-74
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ZHANG L X,LV Y,XU A M,et al.The prognostic signifi⁃ cance of serum gamma ⁃ glutamyltransferase levels and AST/ALT in primary hepatic carcinoma[J].BMC Cancer,2019,19(1):1-9
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LIAO R,WEI X F,CHE P,et al.Nomograms incorpora ⁃ ting the CNLC staging system predict the outcome of he⁃ patocellular carcinoma after curative resection[J].Front Oncol,2022,11:755920
参考文献 22
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目录contents

    摘要

    目的:基于放射学特征和临床实验室数据构建肝细胞癌(hepatocellular carcinoma,HCC)患者发生微血管侵犯(mi- crovascular invasion,MVI)的预测模型,探讨HCC患者术前甲胎蛋白(alpha-fetoprotein,AFP)和γ -谷氨酰转肽酶(gamma-gluta- myl transferase,GGT)的临床应用价值。方法:回顾性地收集南京医科大学第一附属医院479例HCC手术切除患者的临床数据,使用Logistics回归分析确定与MVI显著相关的变量,建立模型并绘制列线图。使用受试者工作特征曲线下面积(area un- der curve,AUC)、校准曲线和决策曲线分析(decision curve analysis,DCA)来评估模型的预测性能。通过灵敏度、特异度和准确度分析对模型预测性能进行比较。应用Kaplan-Meier曲线来评估患者的生存概率。结果:在多因素分析中,肿瘤大小、肿瘤边界不清楚、肿瘤外生性生长、AFP和GGT是HCC患者发生MVI的独立危险因素,用于构建模型。有AFP和GGT的模型A比没有AFP和GGT的模型B表现出更好的预测性能,AUC分别为0.902(0.872~0.932)和0.876(0.842~0.909)。分析评估模型临床净收益的DCA显示出模型A略好于模型B,都具有良好的临床应用价值。模型在队列中显示出良好的识别能力,预测的概率与实际观测值具有较好的一致性。在生存分析中发现,AFP和GGT升高的患者预后明显差于未升高的患者。结论:建立了HCC 患者发生MVI的预测模型,研究证实了血清AFP和GGT的临床应用价值。

    Abstract

    Objective:The current study aims to establish a risk model for microvascular invasion(MVI)in patients with hepatocellular carcinoma(HCC),which is based on radiological characteristics and clinical laboratory data,and to explore the clinical application of preoperative alpha -fetoprotein(AFP)and gamma -glutamyl transferase(GGT)in HCC patients. Methods:Clinical data from 479 HCC patients with surgically resection at the First Affiliated Hospital of Nanjing Medical University were retrospectively collected to investigate the effects of potentially relevant factors on MVI,and to assess the benefits of serum AFP and GGT on improving model predictive performance. Univariate and multivariate logistic regression analyses were used to select variables for the final prediction model,and the predictive performance of the model was assessed by area under the receiver operating characteristic curve(AUC),calibration curve,and decision curve analysis(DCA). Predictive performance was compared by sensitivity,specificity and accuracy analyses. Kaplan - Meier curves were applied to assess the probability of patient survival. Results:In a multifactorial analysis,tumor size,borders of the tumor,exophytic growth,AFP and GGT were independent predictors for the development of MVI in HCC patients. Two models were constructed with or without AFP and GGT,respectively. In both cohorts,model A with AFP and GGT showed better predictive performance than model B without AFP and GGT,with AUCs of 0.902(0.872-0.932)and 0.876(0.842-0.909), respectively. The two models showed good discrimination,and the predicted probabilities were in good agreement with the observed probabilities. Analysis of the DCA,which assesses the net clinical benefit of the models,showed that model A was slightly better than model B,both with good clinical application. In the subgroup analysis,AFP and GGT could well predict the prognosis of HCC patients. Conclusion:A predictive model for the development of MVI in patients with HCC was developed,and the study confirmed the clinical predictive value of serum AFP and GGT.

  • 早期肝细胞癌(hepatocellular carcinoma,HCC) 的有效治疗方法是手术切除和肝移植,接受根治性治疗的HCC患者5年生存率可达70%。然而,HCC 患者的总体生存期并不理想。影响HCC 切除术患者预后的主要原因是肝细胞癌复发和转移的可能性较高[1]。微血管侵犯(microvascular invasion, MVI)通常与肿瘤转移、复发和预后差有关,特别是对可能治愈的手术患者[2]。准确判断 HCC 中 MVI 的存在对临床决策有很大帮助,可以在术前有效地选择手术方案,有利于患者的预后,延长生存期。遗憾的是,目前尚无有效、准确并且简单易行的术前预测方法。因此,本研究基于临床常规检测项目,建立HCC患者MVI的诊断模型,为临床选择治疗方案提供参考。

  • 1 对象和方法

  • 1.1 对象

  • 回顾性分析2009年1月—2017年8月在南京医科大学第一附属医院接受肿瘤切除手术诊断为 HCC的479例患者的临床资料。经南京医科大学第一附属医院伦理委员会批准,符合赫尔辛基宣言的伦理指导方针(批准文号:2021⁃SR⁃253)。

  • 入选标准为:①病理检查确诊为HCC;②术前常规检查资料完整(如常规影像学检测和常规血液学检测);③入院前无手术、化疗、放疗史;④无血液系统疾病、其他神经系统疾病、肝功能障碍等全身性疾病。

  • 1.2 方法

  • 1.2.1 数据收集

  • 回顾性收集2009年1月—2017年8月在南京医科大学第一附属医院因原发性肝癌接受肝切除术的患者。所有肝脏手术都由有经验的肝脏外科医生进行。从临床资料中提取患者的临床信息,包括年龄、性别、影像学资料和病理资料。影像学资料中肿瘤的特征包括肿瘤大小、病灶数目、外生性生长、肿瘤边界、肿瘤中心坏死,肝硬化也纳入分析。这些参数由两名放射科医生独立评估,他们对患者信息一无所知。临床血液学资料包括术前血液常规检查、肿瘤指标检测、生化肝功能检测,都从临床数据资料中提取,包括血清丙氨酸氨基转移酶(ala⁃ nine aminotransferase,ALT)、天冬氨酸氨基转移酶 (aspartate aminotransferase,AST)、γ⁃谷氨酰转肽酶 (gamma⁃glutamyl transferase,GGT)、乳酸脱氢酶(lac⁃ tate dehydrogenase,LDH)、总胆红素(total bilirubin, TB)、直接胆红素(direct bilirubin,DB)、总蛋白(total protein,TP)、白蛋白(albumin,ALB)、肌酐(creati⁃ nine,CR)和乙型肝炎表面抗原(hepatitis B surface antigen,HBsAg)、甲胎蛋白(alpha fetoprotein,AFP)、癌胚抗原(carcinoembryonic antigen,CEA)、糖类抗原19⁃9(carbohydrate antigen19⁃9,CA19⁃9)。淋巴细胞计数(lymphocyte,LY)、中性粒细胞计数(neutro⁃ phil,NE)、血红蛋白(hemoglobin,HB)、红细胞分布宽度变异系数(red cell volume distribution width, RDW)、血小板(platelet,PLT)也包括在内。依据中华人民共和国卫生和计划生育委员会医政医管局在《原发性肝癌诊疗规范(2017 年版)》中的定义, MVI是指在显微镜下于内皮细胞衬覆的脉管腔内见到癌细胞巢团,以门静脉分支为主(含包膜内血管),诊断经有经验的病理专家确认。

  • 1.2.2 随访

  • 所有 HCC 患者术后半年每月随访,前两年每 3 个月随访 1 次,随后几年每 6 个月随访 1 次。每次就诊均完成血常规检测、肝功能检测、肿瘤标志物AFP检测和肝脏超声检查。总生存期定义为手术至死亡或最后一次随访的时间间隔。

  • 1.3 统计学方法

  • 所有统计分析均使用R统计软件(R版本3.6.1 版本.www.r⁃project)进行。不符合正态分布的连续变量以中位数(四分位数)[MP25P75)]表示,并采用非参数检验进行分析。分类资料用频率(百分比)描述,比较采用 Pearson 卡方检验或 Fisher 精确检验。应用单因素和多因素Logistic回归模型评估变量。对单因素分析中P <0.05的变量进行多因素分析。绘制受试者工作特征(receiver operating char⁃ acteristic,ROC)曲线,计算 ROC 曲线下面积(area under curve,AUC),利用决策曲线分析(decision curve analysis,DCA)对不同模型的临床应用进行评价和比较。采用AUC、DCA和校正曲线对列线图性能进行评价。C 指数(C ⁃index)评价 AFP 和 GGT 对 HCC患者总生存的预测能力。绘制ROC曲线确定最佳临界值(cut⁃off),Kaplan⁃Meier曲线分析评估总生存期,并通过Log⁃rank检验进行组间比较。所有统计学检验均为双侧检验,P <0.05为差异有统计学意义。

  • 2 结果

  • 2.1 患者的临床特征

  • 共有479例HCC患者被纳入本研究。男402例 (83.92%),女77例(16.08%),年龄25~86岁,中位年龄为 56 岁。358 例(74.74%)发生肝硬化。144 例 (30.06%)患者观察到 MVI 的存在。MVI 阳性组和阴性组之间年龄、肿瘤大小、病灶数目(单个病灶)、外生性生长、肿瘤边界不清、肿瘤中心坏死、AFP、 CEA、AST、GGT、LDH、LY、NE、RDW、PLT、临床分级及 Edmondson ⁃ Steiner 分级差异均有统计学意义 (P<0.05,表1),其他指标差异均无统计学意义 (P >0.05,表1)。

  • 2.2 预测MVI的Logistic回归分析

  • 如表2所示,通过单因素分析发现,年龄、肿瘤病灶数目、肿瘤外生性生长、肿瘤大小、肿瘤边界不清、肿瘤中心坏死、AFP、AST、GGT、LDH、CR、LY、 RDW、PLT 是 MVI 的危险因素(P <0.05)。将单因素分析结果中 P <0.05 的因素纳入 Logistic 回归模型进行多因素分析,肿瘤外生性生长、肿瘤大小及肿瘤边界不清、AFP和GGT是肝癌合并MVI的独立危险因素。

  • 2.3 MVI预测模型的建立

  • 将多因素分析得到的独立危险因素构建方程。

  • Model A:logit(MVI)=-3.806 93 +0.858 71×肿瘤外生性生长+0.113 94×肿瘤大小+2.620 44×肿瘤边界不清+0.001 45×AFP+0.001 96×GGT

  • Model B:logit(MVI)=-3.240 35+0.812 15×肿瘤外生性生长+0.179 94×肿瘤大小+2.770 83×肿瘤边界不清

  • 由于血清AFP、GGT水平呈现非正态分布,为了减小偏度,将 AFP 和 GGT 值转换为对数值并写成 logAFP和logGGT,使模型具有较好的鉴别性和拟合优度。

  • 2.4 预测模型的验证及诊断性能的评估

  • 对所构建的两个预测模型的诊断性能进行比较(表3),没有AFP和GGT的模型B的AUC为0.876 (0.842~0.909),加入AFP和GGT的模型A的AUC为 0.902(0.872~0.932)(图1A)。特异度由 0.758 提升至0.857,准确度由0.775提升至0.843。阳性似然比和阳性预测值分别由3.360和0.591提升至5.671和 0.709。诊断的比值比也由 13.589 提升至 25.909。从两个模型的DCA也可以看出Model A的最大净效益优于Model B(图1B)。以上结果表明,Model A预测HCC出现MVI,表现出比Model B 更好的预测性能,被选为最终模型。将肿瘤的外生性生长、肿瘤大小、肿瘤边界、AFP和GGT 纳入模型绘制列线图,通过图中每个指标数值对应的分数总和获得 MVI 的预测概率(图2)。为了进一步评价预测模型的预测价值,经 Bootstrap 方法重复抽样 1 000次内部验证。校准图中标准曲线与校准曲线拟合良好,表明模型对 MVI 的预测值与实际观察值之间有良好的符合度,并表现出很好的预测能力(图1C)。

  • 2.5 基于AFP和GGT的HCC患者的生存分析

  • 计算 GGT 和 AFP 的 C 指数(C ⁃index)分别为 0.675(0.631~0.719)和 0.738(0.699~0.776)。使用 ROC曲线确定最佳cut⁃off值,AFP为433.8 ng/mL取 400 ng/mL,GGT为58.7 U/L,绘制Kaplan⁃Meier生存曲线。结果显示 AFP≥400 ng/mL 的患者生存期明显短于AFP<400 ng/mL的患者,GGT≥58.7 U/L的患者生存期明显短于GGT<58.7 U/L的患者(图3,P均 <0.001)。

  • 3 讨论

  • 目前,许多研究致力于MVI的精确诊断和风险评估[3-4]。然而,MVI是微小血管的侵犯,只能在手术后组织病理检查中发现,这限制了MVI在术前管理决策中的使用。近年来,MVI预测模型中影像学指标最常见[5-6],也允许血清标志物加入[7]。但是目前尚不清楚加入的血清标志物对预测模型的影响。基于Logistic回归得出的影像学和血液学标志物的 5 个独立危险因素,本研究建立了两个模型。通过 AUC、DCA、灵敏度、特异度、准确度等评价模型的临床应用价值,发现包含AFP和GGT的模型A 比不包含AFP和GGT的模型B临床表现更好,并最终选择模型A作为MVI的预测模型以绘制列线图。

  • 表1 HCC患者的临床和组织学特征

  • Table1 The clinical and histologic characteristics of HCC patients

  • 本研究中最重要的发现是确认了 AFP 和 GGT 预测 HCC 患者发生 MVI 的价值。通过比较模型 A 和模型B后发现,AFP和GGT加入模型后,阳性预测值、阳性似然比、诊断比值比增加,模型的特异度和准确度都得到提高。AUC 由 0.876 提高到 0.902, DCA中,模型A也好于模型B,两个模型具有良好的临床应用价值和预测性能。此外校准曲线也显示出预测的 MVI 概率与实际 MVI 发生率具有良好的一致性,表明模型具有理想的重复性和可靠性。

  • 表2 HCC患者发生MVI的单因素和多因素Logistic回归分析

  • Table2 Univariate and multivariate logistic regression analysis of HCC patients with MVI

  • 表3 预测模型对MVI的诊断性能

  • Table3 The diagnostic performance of the model for MVI status

  • AFP是HCC早期筛查的主要手段,是诊断常用而又重要的参数,约 70%的 HCC 患者血清 AFP 升高。在《原发性肝癌诊疗规范(2017 年版)》中, AFP≥400 ng/mL 可作为 HCC 的诊断指标之一。肝癌根治性切除标准中明确如术前AFP升高,则术后 2个月AFP水平应回归正常范围。Chen等[8] 综合术前血液测试结果发现当AFP和GGT升高时,患者发生MVI的风险显著增加。对于HCC患者,术前MVI 的预测模型中,AFP作为危险因素多次得到验证[9-10],是预测MVI的可靠指标[11]

  • 研究表明,AFP通过上调转移相关蛋白的表达促进肝癌细胞的侵袭和转移,在促进肝癌转移中起重要作用[12]。Feng 等[13] 研究发现,MVI 的发生和 AFP的升高是HCC患者复发最主要的节点,是患者早复发和实际生存的关键因素。HCC 的预后与 AFP 浓度有关,AFP 值升高预示 HCC 复发风险增高,生存率降低[14]。本研究结果也证实了这一观点。然而,有研究者对此提出了质疑,他们认为血清AFP 可能不是预测HCC患者预后的良好指标[15]。即使 AFP>400 ng/mL,由于其较低的灵敏度和特异度,对于HBV相关HCC患者肝切除术后总生存率和无复发生存率的提示作用不佳[16]。在这种情况下,可以结合 AFP 和其他指标以提高预测肿瘤复发和患者生存的能力[17]

  • 图1 MVI预测模型的诊断预测性能

  • Figure1 The predictive performance of MVI predictive model

  • 图2 术前诊断MVI的列线图

  • Figure2 Nomogram for preoperative estimation of MVI risk

  • 图3 HCC患者的Kaplan⁃Meier生存曲线

  • Figure3 Kaplan⁃Meier survival curve of HCC patients

  • GGT作为一种膜结合的糖蛋白,在谷胱甘肽或相关化合物的γ⁃谷氨酰胺残基的转移中起着关键作用,参与核酸代谢和致癌作用。作为一种氧化应激标志物,与肝细胞外微环境炎症相关。它也是肝胆疾病和肝硬化的一个常见指标,也是一个与HCC相关的生物标志物。最近一项研究表明,在病毒性肝炎患者中,血清GGT水平与HCC的发展呈正相关, GGT可能是一个有用的生物标志物,可以补充AFP 对 HCC 的诊断[18]。Sun 等[19] 通过 meta 分析发现血清GGT水平与较差的总生存率相关,并且与HCC患者的一些临床病理特征密切相关,包括MVI、肿瘤大小和 AFP水平,这与本研究结果一致。

  • Zhang 等[20] 研究也发现,GGT 升高与 HCC 患者较差的预后相关,术前血清GGT可能是HCC患者的一种经济有效的预后生物标志物。然而,GGT的临床应用价值或许被低估,因为它评估肝脏疾病的作用没有ALT和AST等明显,但在多因素分析中,血清 GGT和AFP水平升高通常被认为是影响HCC患者预后的危险因素,本研究结果也是如此。Liao等[21] 发现,GGT 和 AFP 水平是患者生存和肿瘤复发的独立危险因素。此外,Fu等[22] 研究发现,GGT是HCC 切除术后患者有前途的和可靠的预后标志物,特别是在 AFP≤ 200 ng/mL 的患者中,仍能表现出较好的预测作用。这表明,即使血清 AFP 没有超过 400 ng/mL,GGT 也能进一步识别预后不良的患者。在HCC 诊断中,GGT 可能是对AFP 的补充,因此这两个标志物预测HCC 患者预后的临床应用前景令人期待。

  • 本研究也存在一些局限性。首先,本研究是单中心回顾性研究,样本数量受到一定程度的限制, AFP和GGT临床价值评估需要进行大样本的外部验证。再者,在入选标准中,部分患者由于数据不完整而没有纳入总体的分析研究中,可能会存在一定程度的选择偏倚,前瞻性研究可以弥补这一不足。

  • 综上所述,本研究证实了血清AFP和GGT的临床应用价值,并为预测HCC患者的MVI建立了一个全面的预测模型,证实了AFP和GGT对MVI预测模型的贡献。列线图可以帮助临床医生对单个 HCC 患者进行MVI预测和术前治疗方案选择。然而,本研究结果仍然需要更大的样本量来验证,大样本多中心联合研究是未来努力的方向。

  • 参考文献

    • [1] IVANICS T,MURILLO PEREZ C F,CLAASEN M P A W,et al.Dynamic risk profiling of HCC recurrence after curative intent liver resection[J].Hepatology,2022,76(5):1291-1301

    • [2] DU M,CHEN L L,ZHAO J,et al.Microvascular invasion(MVI)is a poorer prognostic predictor for small hepatocel⁃ lular carcinoma[J].BMC Cancer,2014,14(1):1-7

    • [3] BEAUFRERE A,CARUSO S,CALDERARO J,et al.Gene expression signature as a surrogate marker of micro⁃ vascular invasion on routine hepatocellular carcinoma bio⁃ psies[J].J Hepatol,2022,76(2):343-352

    • [4] WANG L Y,WU M L,LI R,et al.MVI⁃mind:a novel deep ⁃ learning strategy using computed tomography(CT)⁃ based radiomics for end⁃to⁃end high efficiency prediction of microvascular invasion in hepatocellular carcinoma [J].Cancers,2022,14(12):2956

    • [5] HONG S B,CHOI S H,KIM S Y,et al.MRI features for predicting microvascular invasion of hepatocellular carci⁃ noma:a systematic review and meta ⁃ analysis[J].Liver Cancer,2021,10(2):94-106

    • [6] LV K,CAO X,DU P,et al.Radiomics for the detection of microvascular invasion in hepatocellular carcinoma[J].World J Gastroenterol,2022,28(20):2176-2183

    • [7] ZHANG J,HUANG S L,XU Y K,et al.Diagnostic accura⁃ cy of artificial intelligence based on imaging data for pre⁃ operative prediction of microvascular invasion in hepato⁃ cellular carcinoma:a systematic review and meta⁃analysis [J].Front Oncol,2022,12:763842

    • [8] CHEN G,WANG R,ZHANG C,et al.Integration of pre⁃ surgical blood test results predict microvascular invasion risk in hepatocellular carcinoma[J].Comput Struct Bio⁃ technol J,2021,19:826-834

    • [9] CHEN S S,WANG C,GU Y W,et al.Prediction of micro⁃ vascular invasion and its M2 classification in hepatocellu⁃ lar carcinoma based on nomogram analyses[J].Front On⁃ col,2022,11:774800

    • [10] LI L,SU Q,YANG H.Preoperative prediction of micro⁃ vascular invasion in hepatocellular carcinoma:a radiomic nomogram based on MRI[J].Clin Radiol,2022,77(4):e269-e279

    • [11] SHI L Q,QU Z,YANG Y,et al.Analysis of related risk factors of microvascular invasion in hepatocellular carci⁃ noma[J].Comput Math Methods Med,2022,2022:1-9

    • [12] LU Y,ZHU M Y,LI W,et al.Alpha fetoprotein plays a critical role in promoting metastasis of hepatocellular car⁃ cinoma cells[J].J Cell Mol Med,2016,20(3):549-558

    • [13] FENG L H,ZHU Y Y,ZHOU J M,et al.A practical risk classification of early recurrence in hepatocellular carci⁃ noma patients with microvascular invasion after hepatecto⁃ my:a decision tree analysis[J].Ann Surg Oncol,2023,30(1):363-372

    • [14] REIG M,FORNER A,RIMOLA J,et al.BCLC strategy for prognosis prediction and treatment recommendation:the 2022 update[J].J Hepatol,2022,76(3):681-693

    • [15] GAO T M,BAI D S,QIAN J J,et al.Preoperative alpha⁃ fetoprotein has no prognostic role in small hepatocellular carcinoma in non⁃cirrhotic patients after surgical resection [J].Turk J Gastroenterol,2021,32(2):133-140

    • [16] YANG S L,LIU L P,YANG S,et al.Preoperative serum α ⁃ fetoprotein and prognosis after hepatectomy for hepato⁃ cellular carcinoma[J].Br J Surg,2016,103(6):716-724

    • [17] NATARPAOLO A,LAYESE R,MAGISTRI P,et al.Vali⁃ dation of the AFP model as a predictor of HCC recurrence in patients with viral hepatitis ⁃ related cirrhosis who had received a liver transplant for HCC[J].J Hepatol,2017,66(3):552-559

    • [18] HUANG C F,YEN M L,TSAI P C,et al.Baseline gam⁃ ma ⁃glutamyl transferase levels strongly correlate with he⁃ patocellular carcinoma development in non ⁃ cirrhotic pa⁃ tients with successful hepatitis C virus eradication[J].J Hepatol,2014,61(1):67-74

    • [19] SUN P,LI Y L,CHANG L J,et al.Prognostic and clinico⁃ pathological significance of gamma⁃glutamyltransferase in patients with hepatocellular carcinoma[J].Medicine,2019,98(19):e15603

    • [20] ZHANG L X,LV Y,XU A M,et al.The prognostic signifi⁃ cance of serum gamma ⁃ glutamyltransferase levels and AST/ALT in primary hepatic carcinoma[J].BMC Cancer,2019,19(1):1-9

    • [21] LIAO R,WEI X F,CHE P,et al.Nomograms incorpora ⁃ ting the CNLC staging system predict the outcome of he⁃ patocellular carcinoma after curative resection[J].Front Oncol,2022,11:755920

    • [22] FU S J,GUO Z Y,LI S Q,et al.Prognostic value of pre⁃ operative serum gamma ⁃ glutamyltranspeptidase in pa⁃ tients with hepatocellular carcinoma after hepatectomy [J].Tumor Biol,2016,37(3):3433-3440

  • 参考文献

    • [1] IVANICS T,MURILLO PEREZ C F,CLAASEN M P A W,et al.Dynamic risk profiling of HCC recurrence after curative intent liver resection[J].Hepatology,2022,76(5):1291-1301

    • [2] DU M,CHEN L L,ZHAO J,et al.Microvascular invasion(MVI)is a poorer prognostic predictor for small hepatocel⁃ lular carcinoma[J].BMC Cancer,2014,14(1):1-7

    • [3] BEAUFRERE A,CARUSO S,CALDERARO J,et al.Gene expression signature as a surrogate marker of micro⁃ vascular invasion on routine hepatocellular carcinoma bio⁃ psies[J].J Hepatol,2022,76(2):343-352

    • [4] WANG L Y,WU M L,LI R,et al.MVI⁃mind:a novel deep ⁃ learning strategy using computed tomography(CT)⁃ based radiomics for end⁃to⁃end high efficiency prediction of microvascular invasion in hepatocellular carcinoma [J].Cancers,2022,14(12):2956

    • [5] HONG S B,CHOI S H,KIM S Y,et al.MRI features for predicting microvascular invasion of hepatocellular carci⁃ noma:a systematic review and meta ⁃ analysis[J].Liver Cancer,2021,10(2):94-106

    • [6] LV K,CAO X,DU P,et al.Radiomics for the detection of microvascular invasion in hepatocellular carcinoma[J].World J Gastroenterol,2022,28(20):2176-2183

    • [7] ZHANG J,HUANG S L,XU Y K,et al.Diagnostic accura⁃ cy of artificial intelligence based on imaging data for pre⁃ operative prediction of microvascular invasion in hepato⁃ cellular carcinoma:a systematic review and meta⁃analysis [J].Front Oncol,2022,12:763842

    • [8] CHEN G,WANG R,ZHANG C,et al.Integration of pre⁃ surgical blood test results predict microvascular invasion risk in hepatocellular carcinoma[J].Comput Struct Bio⁃ technol J,2021,19:826-834

    • [9] CHEN S S,WANG C,GU Y W,et al.Prediction of micro⁃ vascular invasion and its M2 classification in hepatocellu⁃ lar carcinoma based on nomogram analyses[J].Front On⁃ col,2022,11:774800

    • [10] LI L,SU Q,YANG H.Preoperative prediction of micro⁃ vascular invasion in hepatocellular carcinoma:a radiomic nomogram based on MRI[J].Clin Radiol,2022,77(4):e269-e279

    • [11] SHI L Q,QU Z,YANG Y,et al.Analysis of related risk factors of microvascular invasion in hepatocellular carci⁃ noma[J].Comput Math Methods Med,2022,2022:1-9

    • [12] LU Y,ZHU M Y,LI W,et al.Alpha fetoprotein plays a critical role in promoting metastasis of hepatocellular car⁃ cinoma cells[J].J Cell Mol Med,2016,20(3):549-558

    • [13] FENG L H,ZHU Y Y,ZHOU J M,et al.A practical risk classification of early recurrence in hepatocellular carci⁃ noma patients with microvascular invasion after hepatecto⁃ my:a decision tree analysis[J].Ann Surg Oncol,2023,30(1):363-372

    • [14] REIG M,FORNER A,RIMOLA J,et al.BCLC strategy for prognosis prediction and treatment recommendation:the 2022 update[J].J Hepatol,2022,76(3):681-693

    • [15] GAO T M,BAI D S,QIAN J J,et al.Preoperative alpha⁃ fetoprotein has no prognostic role in small hepatocellular carcinoma in non⁃cirrhotic patients after surgical resection [J].Turk J Gastroenterol,2021,32(2):133-140

    • [16] YANG S L,LIU L P,YANG S,et al.Preoperative serum α ⁃ fetoprotein and prognosis after hepatectomy for hepato⁃ cellular carcinoma[J].Br J Surg,2016,103(6):716-724

    • [17] NATARPAOLO A,LAYESE R,MAGISTRI P,et al.Vali⁃ dation of the AFP model as a predictor of HCC recurrence in patients with viral hepatitis ⁃ related cirrhosis who had received a liver transplant for HCC[J].J Hepatol,2017,66(3):552-559

    • [18] HUANG C F,YEN M L,TSAI P C,et al.Baseline gam⁃ ma ⁃glutamyl transferase levels strongly correlate with he⁃ patocellular carcinoma development in non ⁃ cirrhotic pa⁃ tients with successful hepatitis C virus eradication[J].J Hepatol,2014,61(1):67-74

    • [19] SUN P,LI Y L,CHANG L J,et al.Prognostic and clinico⁃ pathological significance of gamma⁃glutamyltransferase in patients with hepatocellular carcinoma[J].Medicine,2019,98(19):e15603

    • [20] ZHANG L X,LV Y,XU A M,et al.The prognostic signifi⁃ cance of serum gamma ⁃ glutamyltransferase levels and AST/ALT in primary hepatic carcinoma[J].BMC Cancer,2019,19(1):1-9

    • [21] LIAO R,WEI X F,CHE P,et al.Nomograms incorpora ⁃ ting the CNLC staging system predict the outcome of he⁃ patocellular carcinoma after curative resection[J].Front Oncol,2022,11:755920

    • [22] FU S J,GUO Z Y,LI S Q,et al.Prognostic value of pre⁃ operative serum gamma ⁃ glutamyltranspeptidase in pa⁃ tients with hepatocellular carcinoma after hepatectomy [J].Tumor Biol,2016,37(3):3433-3440