Predictive value of preoperative imaging tumor burden score-based model for microvascular invasion of single hepatocellular carcinoma

doi:10.3877/cma.j.issn.2095-3232.2026.03.012

Abstract

Abstract:

Objective

To evaluate the predictive value of model based on preoperative imaging tumor burden score (ITBS) for microvessel invasion (MVI) of single hepatocellular carcinoma (HCC).

Methods

Clinical data of 213 patients with single HCC who underwent surgical resection in the 900th Hospital from January 2015 to December 2021 were retrospectively analyzed. Among them, 148 patients were male and 65 female, aged (53±12) years. The predictive value of ITBS for MVI was assessed by the receiver operating characteristic (ROC) curve. The threshold value of ITBS was determined. All patients were divided into the high and low ITBS groups. Univariate and multivariate Logistic regression analyses were used to analyze the correlation with MVI. Nomogram model was constructed based on Akachi Information Criterion (AIC). The consistency and predictive ability of this model were validated by the calibration curve and ROC curve. The application value of prediction model in actual clinical decision was evaluated by the decision curve analysis (DCA).

Results

The ITBS value of HCC patients was 1.28-18.03. The area under the ROC curve (AUC) of ITBS for predicting MVI in HCC patients was 0.681 (95%CI: 0.592-0.769), the sensitivity was 0.647, the specificity was 0.710, and the optimal threshold value was 5.9, respectively. According to this threshold value of ITBS, all patients were divided into the low (n=132) and high ITBS groups (n=81). Multivariate Logistic regression analysis showed that male gender, complicated with diabetes mellitus, low ALB and low Child-Pugh score were the risk factors for MVI in HCC patients (OR=2.432, 2.545, 0.919, 5.158; all P<0.05). Based on independent risk factors and ITBS, nomogram model was constructed according to AIC. ROC curve analysis showed that the AUC, sensitivity and specificity of the nomogram model were 0.767, 0.608 and 0.809, indicating high prediction efficiency. The calibration curve demonstrated that the the probability predicted by the nomogram model was consistent with the actual results. External validation also suggested the reliability of this nomogram model. DCA curve showed that the nomogram model can be utilized as a valuable prediction tool for the risk of MVI in patients with single HCC.

Conclusions

ITBS can predict the occurrence of MVI in patients with single HCC. The nomogram model based on gender, diabetes mellitus, ALB, Child-Pugh class and ITBS can reliably and intuitively predict the risk of MVI in patients with single HCC.

Key words: Carcinoma, hepatocellular, Microvascular invasion, Nomogram, Imaging tumour load score

Zhijian Chen, Jianda Yu, Zerun Lin, Honghuan Lin, Changyu Yi, Xiaobin Chi, Lizhi Lyu, Yongbiao Chen. Predictive value of preoperative imaging tumor burden score-based model for microvascular invasion of single hepatocellular carcinoma[J]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(03): 379-385.

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Figures/Tables 5

项目	低ITBS组	高ITBS组	统计值	P值
性别[例, 男/女]	89/42	59/22	χ²=0.69	0.404
WBC(×10⁹/L, $x ¯$ ±s)	6.3±2.2	6.6±2.0	t=1.06	0.292
淋巴细胞(×10⁹/L, $x ¯$ ±s)	1.8±0.60	1.7±0.6	t=1.38	0.169
RBC(×10¹²/L, $x ¯$ ±s)	4.7±0.5	4.7±0.5	t=0.26	0.792
Hb(g/L, $x ¯$ ±s)	141±17	141±14	t=0.23	0.805
Plt(×10⁹/L, $x ¯$ ±s)	183±74	203±81	t=1.85	0.066
AFP>400 μg/L[例(%)]	34(26)	32(40)	χ²=4.44	0.035
糖尿病[例(%)]	100(76)	64(79)	χ²=0.69	0.404
乙肝[例(%)]	17(46)	14(35)	χ²=2.45	0.117
HBV-DNA>500(IU/ml)	72(55)	53(65)	χ²=2.45	0.117
ALT[U/L, M(Q₁, Q₃)]	35 (26, 44)	35 (24, 55)	Z=1.27	0.206
AST[U/L, M(Q₁, Q₃)]	33(23, 50)	33 (23, 51)	Z=4.92	<0.001
ALB(g/L, $x ¯$ ±s)	43±5	42±5	t=2.23	0.027
TB(μmmol/L, $x ¯$ ±s)	13±9	15±11	t=0.90	0.368
PT(s, $x ¯$ ±s)	11.7±1.0	11.9±1.0	t=1.47	0.143
肿瘤最大径(cm, $x ¯$ ±s)	3.4±1.3	9.1±2.6	t=21.53	<0.001
MVI[例(%)]	18(16)	33(41)	χ²=18.79	<0.001
Child-Pugh分级B级[例(%)]	4(3)	9(11)	χ²=4.40	0.036
肝硬化[例(%)]	21(16)	19(24)	χ²=1.41	0.235

项目	低ITBS组	高ITBS组	统计值	P值
性别[例, 男/女]	89/42	59/22	χ²=0.69	0.404
WBC(×10⁹/L, $x ¯$ ±s)	6.3±2.2	6.6±2.0	t=1.06	0.292
淋巴细胞(×10⁹/L, $x ¯$ ±s)	1.8±0.60	1.7±0.6	t=1.38	0.169
RBC(×10¹²/L, $x ¯$ ±s)	4.7±0.5	4.7±0.5	t=0.26	0.792
Hb(g/L, $x ¯$ ±s)	141±17	141±14	t=0.23	0.805
Plt(×10⁹/L, $x ¯$ ±s)	183±74	203±81	t=1.85	0.066
AFP>400 μg/L[例(%)]	34(26)	32(40)	χ²=4.44	0.035
糖尿病[例(%)]	100(76)	64(79)	χ²=0.69	0.404
乙肝[例(%)]	17(46)	14(35)	χ²=2.45	0.117
HBV-DNA>500(IU/ml)	72(55)	53(65)	χ²=2.45	0.117
ALT[U/L, M(Q₁, Q₃)]	35 (26, 44)	35 (24, 55)	Z=1.27	0.206
AST[U/L, M(Q₁, Q₃)]	33(23, 50)	33 (23, 51)	Z=4.92	<0.001
ALB(g/L, $x ¯$ ±s)	43±5	42±5	t=2.23	0.027
TB(μmmol/L, $x ¯$ ±s)	13±9	15±11	t=0.90	0.368
PT(s, $x ¯$ ±s)	11.7±1.0	11.9±1.0	t=1.47	0.143
肿瘤最大径(cm, $x ¯$ ±s)	3.4±1.3	9.1±2.6	t=21.53	<0.001
MVI[例(%)]	18(16)	33(41)	χ²=18.79	<0.001
Child-Pugh分级B级[例(%)]	4(3)	9(11)	χ²=4.40	0.036
肝硬化[例(%)]	21(16)	19(24)	χ²=1.41	0.235

因素	单因素分析		多因素分析
因素	OR(95%CI)	P值	OR(95%CI)	P值
男性	2.465(1.120~5.429)	0.025	2.432(1.030~5.742)	0.043
糖尿病	2.031(1.010~4.086)	0.047	2.545(1.130~5.732)	0.024
HBV-DNA>500(IU/ml)	1.969(1.001~3.875)	0.049	1.512(0.694~3.295)	0.298
ALB(g/L)	0.909(0.847~0.976)	0.009	0.919(0.847~0.996)	0.039
肿瘤最大径(cm)	1.192(1.085~1.309)	<0.001	1.052(0.888~1.245)	0.558
Child-Pugh分级B级	5.842(1.818~18.769)	0.003	5.158(1.408~18.891)	0.013
ITBS>5.9	4.354(2.237~8.475)	<0.001	2.764(0.819~9.335)	0.102

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