基于超声造影LI-RADS特征的肝细胞癌微血管侵犯列线图模型的构建及验证

奚静; 顾美琴; 包作伟

doi:10.3969/j.issn.1001-5256.2022.11.016

基于超声造影LI-RADS特征的肝细胞癌微血管侵犯列线图模型的构建及验证

DOI: 10.3969/j.issn.1001-5256.2022.11.016

奚静¹,
顾美琴^1, , ,,
包作伟²

1.
江苏大学附属武进医院(徐州医科大学武进临床学院) 超声科，江苏常州 213000
2.
常州市第三人民医院超声科，江苏常州 213000

基金项目:

江苏省卫生健康委2019年度医学科研面上项目 (H201982)

伦理学声明：本研究方案于2016年12月17日经由江苏大学附属武进医院伦理委员会审批，批号：2016-P2-010，所纳入患者均签署知情同意书。

利益冲突声明：本研究不存在研究者、伦理委员会成员、受试者监护人以及与公开研究成果有关的利益冲突。

作者贡献声明：奚静负责课题设计，资料分析，撰写论文；顾美琴、包作伟参与收集数据，修改论文。

详细信息

通信作者:
顾美琴，gu8210mq@126.com

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出版历程
- 收稿日期: 2022-04-12
- 录用日期: 2022-06-15
- 出版日期: 2022-11-20

Construction and validation of a nomogram model for microvascular invasion in hepatocellular carcinoma based on the characteristics on contrast-enhanced ultrasound Liver Imaging Reporting and Data System

Jing XI¹,
Meiqin GU^{1
, ,
,},
Zuowei BAO²

1.
Department of Ultrasound, Wujin Hospital Affiliated to Jiangsu University, Wujin Clinical College, Xuzhou Medical University, Changzhou, Jiangsu 213000, China
2.
Department of Ultrasound, Changzhou Third People's Hospital, Changzhou, Jiangsu 213000, China

Research funding:

2019 General Medical Research Project of Jiangsu Provincial Health Commission (H201982)

More Information

Corresponding author: GU Meiqin, gu8210mq@126.com(ORCID: 0000-0002-8938-5343)

摘要

摘要: 目的基于肝细胞癌(HCC)患者的超声造影(CEUS)肝脏成像报告和数据系统(LI-RADS)特征建立预测微血管侵犯(MVI)的列线图模型并进行验证。方法选取2017年1月—2020年7月在江苏大学附属武进医院确诊的HCC患者共262例，按照1∶ 1比例随机分为建模组和验证组各131例，以术后镜下病理结果确诊MVI，其中建模组MVI 70例和验证组MVI 56例。采用超声造影评估两组的LI-RADS特征。两组间计量资料比较采用独立样本t检验；两组间计数资料比较采用χ²检验。采用单因素和多因素Logistic回归分析筛选建模组MVI的危险因素；绘制受试者工作特征曲线(ROC曲线)，计算模型预测MVI的曲线下面积(AUC)，评估预测准确度；应用决策曲线分析模型的一致性，比较模型预测MVI的校正曲线与标准曲线的离散度。结果建模组与验证组患者的临床资料和CEUS检查结果比较，差异均无统计学意义(P值均＞0.05)。单因素分析显示，与MVI阴性患者相比，MVI阳性患者血清AFP水平显升高，肿瘤直径增大，LI-RADS显示LR-5“后出”和LR-M“先出”增多，LI-RADS分级较高，差异均有统计学意义(P值均＜0.05)。多因素分析显示，AFP 20~400 ng/mL(OR=2.65，P＜0.001)、AFP≥400 ng/mL(OR=3.98，P＜0.001)、肿瘤直径≥30 mm(OR=2.12，P＜0.001)和CEUS显示LR-M(OR=3.24，P＜0.001) 是MVI的独立危险因素。ROC曲线显示，列线图预测建模组和验证组MVI的AUC分别为0.867和0.821。列线图模型的一致性指数C-Index为0.765(95%CI：0.701~0.834)。在建模组和验证组，列线图模型的预测校准曲线与标准曲线均接近。结论利用CEUS得出LI-RADS，并结合AFP和肿瘤直径建立的列线图模型有较好的应用价值，有助于指导临床术前筛选MVI高危患者，制订恰当的手术方案。
- 癌, 肝细胞 /
- 微血管侵犯 /
- 超声造影 /
- 列线图
Abstract: Objective To construct and validate a nomogram model for predicting microvascular invasion (MVI) based on the characteristics on contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) in patients with hepatocellular carcinoma (HCC). Methods A total of 262 patients with HCC who were diagnosed in Wujin Hospital Affiliated to Jiangsu University from January 2017 to July 2020 were enrolled and randomly divided into modeling group and validation group at a ratio of 1∶ 1, with 131 patients in each group. MVI was confirmed by postoperative microscopic pathological results, and there were 70 patients with MVI in the modeling group and 56 patients with MVI in the validation group. CEUS was used to evaluate LI-RADS characteristics for the two groups. The independent samples t-test was used for comparison of continuous data between the two groups, and the chi-square test was used for comparison of categorical data between the two groups. Univariate and multivariate Logistic regression analyses were used to identify the risk factors for MVI in the modeling group; the receiver operating characteristic (ROC) curve was plotted, and the area under the ROC curve (AUC) was calculated for the model in predicting MVI to evaluate the accuracy of prediction; a decision curve analysis was used to evaluate the consistency of the model, and dispersion was compared between the calibration curve and the standard curve for the model in predicting MVI. Results There were no significant differences in clinical data and CEUS findings between the modeling group and the validation group (all P > 0.05). The univariate analysis showed that compared with the MVI-negative patients, the MVI-positive patients had significant increases in serum alpha fetoprotein (AFP) level, tumor diameter, and LR-5 "late and mild washout" and LR-M "early washout" on LI-RADS, as well as a significantly higher LI-RADS grade (all P < 0.05). The multivariate analysis showed that AFP 20-400 ng/mL (odds ratio [OR]=2.65, P < 0.001), AFP≥400 ng/mL (OR=3.98, P < 0.001), tumor diameter ≥30 mm (OR=2.12, P < 0.001), and LR-M on CEUS (OR=3.24, P < 0.001) were independent risk factors for MVI. The ROC curve analysis showed that the nomogram had an AUC of 0.867 and 0.821 in predicting MVI in the modeling group and the validation group, respectively. The nomogram model had a C-Index of 0.765 (95% confidence interval: 0.701-0.834). The calibration curves of the nomogram model were close to the standard curve in both groups. Conclusion The nomogram model based on LI-RADS obtained by CEUS in combination with AFP and tumor diameter has a good application value and can guide the preoperative screening for patients at a high risk of MVI and the development of appropriate surgical plans in clinical practice.
- Carcinoma, Hepatocellular /
- Microvascular Invasion /
- Contrast-Enhanced Ultrasound /
- Nomograms

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图 1 HCC的CEUS图像分析

注：a，肝硬化患者超声显示肿瘤直径35.5 mm，低回声(虚线处)；b，MVI阴性，LR-5动脉期增强和“后出”征象(虚线处)；c，MVI阳性，LR-M边缘增强和“先出”征象(虚线处)；d，病理标本显示分化良好，无MVI (HE染色，×100)。

Figure 1. CEUS image of HCC

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图 2 MVI的列线图模型

Figure 2. Nomograph model of MVI

下载: 全尺寸图片幻灯片

图 3 列线图模型预测建模组和验证组MVI的ROC曲线分析

注：a，建模组；b，验证组。

Figure 3. ROC analysis of nomograph model for predicting MVI in model group and validation group

下载: 全尺寸图片幻灯片

图 4 建模组与验证组的列线图预测曲线

注：a，建模组；b，验证组。

Figure 4. Nomograph predictive curve of model group and validation group

下载: 全尺寸图片幻灯片

表 1 建模组与验证组的临床资料和CEUS特征比较

Table 1. Comparison of clinical datas and CEUS characteristics between model group and validation group

指标	建模组(n=131)	验证组(n=131)	统计值	P值
男/女(例)	78/53	70/61	χ²=0.994	0.319
年龄(岁)	58.2±6.7	57.2±6.3	t=1.052	0.198
乙型肝炎[例(%)]	115(87.8)	110(84.0)	χ²=0.787	0.375
肝硬化[例(%)]	80(61.1)	84(64.1)	χ²=0.261	0.610
肿瘤TNM分期[例(%)]			χ²=0.138	0.710
Ⅰ~Ⅱ	60(45.8)	63(48.1)
Ⅲ~Ⅳ	71(54.2)	68(51.9)
分化级别[例(%)]			χ²=0.245	0.621
低	65(49.6)	61(46.6)
中高	66(50.4)	70(53.4)
MVI[例(%)]	70(53.4)	56(42.7)	χ²=2.997	0.083
血生化指标
PT＞14 s[例(%)]	42(32.1)	33(25.2)	χ²=1.513	0.219
AFP(ng/mL)	365±74	349±68	t=0.795	0.302
PLT(×10⁹/L)	201±32	212±39	t=0.632	0.348
TBil(μmol/L)	15.6±3.8	13.9±3.2	t=0.469	0.521
Alb(g/L)	35.9±4.5	33.2±4.3	t=0.396	0.627
ALT(U/L)	40.2±5.3	43.9±5.7	t=0.559	0.501
二维超声
肿瘤直径≥30 mm[例(%)]	92(70.2)	81(61.8)	χ²=2.059	0.151
回声类型[例(%)]			χ²=0.177	0.674
低	95(72.5)	98(74.8)
等或高	36(27.5)	33(25.2)
边缘不清[例(%)]	101(77.1)	93(71.0)	χ²=1.271	0.260
不规则外形[例(%)]	92(70.2)	94(71.8)	χ²=0.074	0.785
Halo征[例(%)]	32(24.4)	28(21.4)	χ²=0.346	0.556
血管分布[例(%)]			χ²=0.078	0.780
少	97(74.0)	95(72.5)
多	34(26.0)	36(27.5)
CEUS特征[例(%)]
LR-5动脉期增强	93(71.0)	89(67.9)	χ²=0.288	0.592
“后出”征象	57(43.5)	51(38.9)	χ²=0.567	0.451
LR-M边缘增强	19(14.5)	22(16.8)	χ²=0.260	0.610
“先出”征象	78(59.5)	71(54.2)	χ²=0.762	0.383
LI-RADS分级[例(%)]			χ²=0.492	0.782
3~4	16(12.3)	19(14.5)
5	48(36.6)	50(38.2)
M	67(51.1)	62(47.3)
镶嵌图形征[例(%)]	23(17.6)	25(19.1)	χ²=0.102	0.749
结节内结节征[例(%)]	20(15.3)	17(13.0)	χ²=0.283	0.595

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表 2 MVI的危险因素分析

Table 2. Risk factor analysis of MVI

因素	单因素分析			多因素分析
因素	OR	95%CI	P值	OR	95%CI	P值
AFP
20~400 ng/mL	3.19	2.66~3.42	＜0.001	2.65	2.13~3.23	＜0.001
≥400 ng/mL	4.45	3.78~4.79	＜0.001	3.98	3.32~4.39	＜0.001
肿瘤直径≥30 mm	2.76	2.23~3.16	＜0.001	2.12	1.69~2.58	＜0.001
LR-5“后出”	2.09	1.56~2.54	＜0.001
LR-M“先出”	3.96	3.46~4.49	＜0.001	3.24	2.78~3.65	＜0.001
LI-RADS分级	2.01	1.48~2.43	＜0.001

下载: 导出CSV

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