Construction of preoperative prediction model for microvascular invasion of hepatocellular carcinoma based on Meta-analysis

doi:10.3877/cma.j.issn.2095-3232.2025011

Abstract

Abstract:

Objective

To construct a preoperative prediction model for microvascular invasion(MVI) of hepatocellular carcinoma (HCC) based on Meta-analysis.

Methods

Studies related to prediction model for MVI of HCC published at home and abroad was retrieved.The retrieval results were screened and the data were extracted.The quality of included studies was assessed by Newcastle-Ottawa Scale (NOS).Meta-analysis was carried out for the included studies.The risk factors with significant combined effects were extracted and the risk values were combined according to the results.The prediction model was constructed and a nomogram was delineated.64 HCC patients undergoing surgery in Chifeng Municipal Hospital from January 2020 to January 2023 were selected into the model validation set.The prediction performance of this model was evaluated by the area under the ROC curve (AUC).The accuracy and clinical practicability of this model were analyzed by the calibration curve and decision curve analysis.

Results

A total of 20 cohort studies consisting of 4 021 HCC patients were included.In the Meta-analysis of these 20 studies, 7 risk factors were finally screened after combined effects, including AFP (OR=1.38, 95%CI: 1.22-1.54), GGT (OR=1.49, 95%CI: 1.14-1.83)and ALB (OR=0.90, 95%CI: 0.78-1.02), intratumoral artery (OR=3.48, 95%CI: 3.12-3.85), peritumoral enhancement (OR=2.49, 95%CI: 1.94-3.04), tumor diameter (OR=2.93, 95%CI: 2.59-3.27) and extrahepatic growth (OR= 2.08, 95%CI: 1.75-2.42), respectively.7 risk factors were divided into the imaging feature group,laboratory index group and combined group, and assigned with values, and 3 models were constructed to predict early postoperative recurrence of HCC.The AUC of the model in the combined group was 0.877, and the sensitivity and specificity were 0.813 and 0.875, respectively.The prediction performance of this model was almost consistent with the actual values, indicating high clinical benefits.

Conclusions

Meta-analysis demonstrates that the risk factors of MVI in HCC include AFP, GGT, ALB, intratumoral artery, peritumoral enhancement, tumor diameter and extrahepatic growth.The prediction model for MVI risk based on Metaanalysis yields favorable prediction performance, which can be used as an assessment tool for the risk of MVI.

Key words: Carcinoma, hepatocellular, Microvascular invasion, Meta-analysis, Prediction model

Xin Li, Le Li, Jinming Chen, Rui Liu. Construction of preoperative prediction model for microvascular invasion of hepatocellular carcinoma based on Meta-analysis[J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2025, 14(01): 74-80.

/ / Recommend

Add to citation manager EndNote|Ris|BibTeX

URL: https://zhgzwkssxdzzz.cma-cmc.com.cn/EN/10.3877/cma.j.issn.2095-3232.2025011

https://zhgzwkssxdzzz.cma-cmc.com.cn/EN/Y2025/V14/I01/74

Figures/Tables 5

References 36

[1]	Marrero JA, Welling T.Modern diagnosis and management of hepatocellular carcinoma[J].Clin Liver Dis, 2009, 13(2):233-247.
[2]	Yuen MF, Hou JL, Chutaputti A, et al.Hepatocellular carcinoma in the Asia Pacific region[J].J Gastroenterol Hepatol, 2009, 24(3):346-353.
[3]	Tabrizian P, Jibara G, Shrager B, et al.Recurrence of hepatocellular cancer after resection: patterns, treatments, and prognosis[J].Ann Surg, 2015, 261(5):947-955.
[4]	Zhang C, Zhao R, Chen F, et al.Preoperative prediction of microvascular invasion in non-metastatic hepatocellular carcinoma based on nomogram analysis[J].Transl Oncol, 2021, 14(1):100875.
[5]	Lin S, Ye F, Rong W, et al.Nomogram to assist in surgical plan for hepatocellular carcinoma: a prediction model for microvascular invasion[J].J Gastrointest Surg, 2019, 23(12):2372-2382.
[6]	Zheng J, Chakraborty J, Chapman WC, et al.Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis[J].J Am Coll Surg, 2017, 225(6):778-788, e1.
[7]	Zhou JM, Zhou CY, Chen XP, et al.Anatomic resection improved the long-term outcome of hepatocellular carcinoma patients with microvascular invasion: a prospective cohort study[J].World J Gastrointest Oncol, 2021, 13(12):2190-2202.
[8]	Wang Z, Ren Z, Chen Y, et al.Adjuvant transarterial chemoembolization for HBV-related hepatocellular carcinoma after resection: a randomized controlled study[J].Clin Cancer Res, 2018, 24(9):2074-2081.
[9]	Shi C, Li Y, Geng L, et al.Adjuvant stereotactic body radiotherapy after marginal resection for hepatocellular carcinoma with microvascular invasion: a randomised controlled trial[J].Eur J Cancer,2022, 166:176-184.
[10]	Erstad DJ, Tanabe KK.Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma[J].Ann Surg Oncol, 2019, 26(5):1474-1493.
[11]	Rodríguez-Perálvarez M, Luong TV, Andreana L, et al.A systematic review of microvascular invasion in hepatocellular carcinoma:diagnostic and prognostic variability[J].Ann Surg Oncol, 2013,20(1):325-339.
[12]	Jiang H, Wei J, Fu F, et al.Predicting microvascular invasion in hepatocellular carcinoma: a dual-institution study on gadoxetate disodium-enhanced MRI[J].Liver Int, 2022, 42(5):1158-1172.
[13]	Jiang W, Wang J, Shen X, et al.Establishment and validation of a risk prediction model for early diabetic kidney disease based on a systematic review and meta-analysis of 20 cohorts[J].Diabetes Care, 2020,43(4):925-933.
[14]	陈晓辉, 吴淼, 陈宇驰, 等.增强CT影像学征象联合判断肝细胞癌微血管侵犯的价值研究[J].中国医学装备, 2022, 19(3):80-84.
[15]	邓家仲, 荚卫东.肝细胞癌微血管侵犯危险因素分析及术前预测列线图模型构建[J].中国普通外科杂志, 2021, 30(7):772-779.
[16]	强光辉, 曹胤, 张广, 等.肝癌微血管侵犯对预后的影响及基于γ-谷氨酰转肽酶-淋巴细胞比值预测评分模型的建立[J].中国临床研究,2021, 34(9):1164-1170.
[17]	田雅琪, 胡亚彬, 彭琪琪, 等.钆塞酸二钠增强MRI列线图模型预测小肝癌微血管侵犯的价值[J].磁共振成像, 2021, 12(10):57-60, 65.
[18]	王静, 征锦, 孙骏, 等.钆塞酸二钠增强MRI对肝细胞癌微血管侵犯及其分级的预测价值[J].磁共振成像, 2021(12):79-83, 88.
[19]	张泳欣, 张水兴, 肖学红, 等.基于影像学和血清学特征构建的列线图模型可较好预测原发性肝细胞癌的微血管浸润[J].分子影像学杂志, 2022, 45(4):518-525.
[20]	赵江生.肝癌合并微血管侵犯的相关因素分析及预测模型的评价与建立[D].南京: 南京医科大学, 2021.
[21]	左立平, 蒋丰洋, 周斌彬, 等.术前MRI在预测169例肝细胞肝癌微血管侵犯及早期复发的价值[J].山东大学学报(医学版), 2022,60(3):89-95, 99.
[22]	Chen J, Zhou J, Kuang S, et al.Liver imaging reporting and data system category 5: MRI predictors of microvascular invasion and recurrence after hepatectomy for hepatocellular carcinoma[J].AJR Am J Roentgenol, 2019, 213(4):821-830.
[23]	Lu XY, Zhang JY, Zhang T, et al.Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI[J].BMC Med Imaging, 2022,22(1):157.
[24]	Mao S, Yu X, Yang Y, et al.Preoperative nomogram for microvascular invasion prediction based on clinical database in hepatocellular carcinoma[J].Sci Rep, 2021, 11(1):13999.
[25]	Wang H, Lu Y, Liu R, et al.A non-invasive nomogram for preoperative prediction of microvascular invasion risk in hepatocellular carcinoma[J].Front Oncol, 2021, 11:745085.
[26]	Wang L, Jin YX, Ji YZ, et al.Development and validation of a prediction model for microvascular invasion in hepatocellular carcinoma[J].World J Gastroenterol, 2020, 26(14):1647-1659.
[27]	Xu T, Ren L, Liao M, et al.Preoperative radiomics analysis of contrast-enhanced CT for microvascular invasion and prognosis stratification in hepatocellular carcinoma[J].J Hepatocell Carcinoma,2022, 9:189-201.
[28]	Yang L, Gu D, Wei J, et al.A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J].Liver Cancer, 2019, 8(5):373-386.
[29]	Yue Q, Zhou Z, Zhang X, et al.Contrast-enhanced CT findings-based model to predict MVI in patients with hepatocellular carcinoma[J].BMC Gastroenterol, 2022, 22(1):544.
[30]	Xu X, Zhang HL, Liu QP, et al.Radiomic analysis of contrastenhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma[J].J Hepatol, 2019, 70(6):1133-1144.
[31]	丁丁, 杨云川, 马翔, 等.术前C-反应蛋白-白蛋白-淋巴细胞比值在肝细胞癌预后中的价值评估[J/OL].中华普通外科学文献(电子版),2024, 18(4):261-265.
[32]	叶莹鹏, 杨勇, 蔡星晨, 等.单发大肝癌术后早期复发危险因素分析[J].中华普通外科杂志, 2023, 38(1):1-6.
[33]	梁志银, 陈长志, 黄韬, 等.肝细胞癌微血管侵犯术前预测列线图的建立与应用[J].中华肝胆外科杂志, 2019, 25(5):344-348.
[34]	胡光超, 张倩倩, 毛宁, 等.术前MRI预测肝细胞癌微血管侵犯研究进展[J].磁共振成像, 2022, 13(2):159-162.
[35]	Nino-Murcia M, Olcott EW, Jeffrey RB Jr, et al.Focal liver lesions:pattern-based classification scheme for enhancement at arterial phase CT[J].Radiology, 2000, 215(3):746-751.
[36]	Segal E, Sirlin CB, Ooi C, et al.Decoding global gene expression programs in liver cancer by noninvasive imaging[J].Nat Biotechnol,2007, 25(6):675-680.

实验室指标				影像学指标
危险因素	文献数	OR（95%CI）	异质性	危险因素	文献数	OR（95%CI）	异质性
AFP	12	2.60（2.07～3.12）	I ²=95.5%，P＜0.01	肿瘤直径	12	1.40（1.27～1.53）	I ²=93.0%，P＜0.01
GLR	1	1.69（1.10～2.60）		多灶肿瘤	5	2.51（1.65～3.37）	I ²=92.2%，P＜0.01
NLR	1	1.86（1.18～2.93）		肝外生长	4	2.81（1.79～3.82）	I ²=87.7%，P＜0.01
ALB	3	6.28（0.94～11.62）	I ²=99.9%，P＜0.01	假包膜	7	5.54（2.90～8.39）	I ²=99.1%，P＜0.01
GGT	3	1.82（1.07～2.57）	I ²=85.2%，P＜0.01	肝硬化	1	2.03（1.22～3.36）
Plt	1	2.74（1.19～6.32）		不光滑表面	9	3.34（2.68～4.01）	I ²=88.8%，P＜0.01
VEGF	1	33.08（12.87～85.05）		明显弥散受限	1	3.00（1.70～5.50）
TB	1	2.24（1.03～4.86）		瘤内动脉	5	4.29（2.94～5.64）	I ²=94.7%，P＜0.01
AST	4	1.63（0.86～2.39）	I ²=92.2%，P＜0.01	瘤周弱化	3	6.07（1.70～10.49）	I ²=97.9%，P＜0.01
				瘤周强化	4	2.81（2.08～3.53）	I ²=53.6%，P=0.091
				马赛克征	1	1.48（0.75～2.93）
				无脂肪堆积	1	2.10（1.15～3.85）

实验室指标				影像学指标
危险因素	文献数	OR（95%CI）	异质性	危险因素	文献数	OR（95%CI）	异质性
AFP	12	2.60（2.07～3.12）	I ²=95.5%，P＜0.01	肿瘤直径	12	1.40（1.27～1.53）	I ²=93.0%，P＜0.01
GLR	1	1.69（1.10～2.60）		多灶肿瘤	5	2.51（1.65～3.37）	I ²=92.2%，P＜0.01
NLR	1	1.86（1.18～2.93）		肝外生长	4	2.81（1.79～3.82）	I ²=87.7%，P＜0.01
ALB	3	6.28（0.94～11.62）	I ²=99.9%，P＜0.01	假包膜	7	5.54（2.90～8.39）	I ²=99.1%，P＜0.01
GGT	3	1.82（1.07～2.57）	I ²=85.2%，P＜0.01	肝硬化	1	2.03（1.22～3.36）
Plt	1	2.74（1.19～6.32）		不光滑表面	9	3.34（2.68～4.01）	I ²=88.8%，P＜0.01
VEGF	1	33.08（12.87～85.05）		明显弥散受限	1	3.00（1.70～5.50）
TB	1	2.24（1.03～4.86）		瘤内动脉	5	4.29（2.94～5.64）	I ²=94.7%，P＜0.01
AST	4	1.63（0.86～2.39）	I ²=92.2%，P＜0.01	瘤周弱化	3	6.07（1.70～10.49）	I ²=97.9%，P＜0.01
				瘤周强化	4	2.81（2.08～3.53）	I ²=53.6%，P=0.091
				马赛克征	1	1.48（0.75～2.93）
				无脂肪堆积	1	2.10（1.15～3.85）

分组	危险因素	OR	95%CI	β	分值
实验室指标	AFP	1.38	1.22～1.54	3.17	3
	GGT	1.49	1.14～1.83	3.43	3
	ALB	0.90	0.78～1.02	2.07	2
影像特征	肝外生长	2.08	1.75～2.42	4.78	5
	瘤内动脉	3.48	3.12～3.85	8.01	8
	瘤周强化	2.49	1.94～3.04	5.73	6
	肿瘤直径	2.93	2.59～3.27	6.74	7

分组	危险因素	OR	95%CI	β	分值
实验室指标	AFP	1.38	1.22～1.54	3.17	3
	GGT	1.49	1.14～1.83	3.43	3
	ALB	0.90	0.78～1.02	2.07	2
影像特征	肝外生长	2.08	1.75～2.42	4.78	5
	瘤内动脉	3.48	3.12～3.85	8.01	8
	瘤周强化	2.49	1.94～3.04	5.73	6
	肿瘤直径	2.93	2.59～3.27	6.74	7

Please choose a citation manager

Content to export