基于术前影像肿瘤负荷评分的模型对单发肝细胞癌微血管侵犯的预测价值

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

中华肝脏外科手术学电子杂志 ›› 2026, Vol. 15 ›› Issue (03) : 379 -385. doi: 10.3877/cma.j.issn.2095-3232.2026.03.012

临床研究

基于术前影像肿瘤负荷评分的模型对单发肝细胞癌微血管侵犯的预测价值

陈志坚¹, 俞建达², 林泽润², 林宏焕², 易长昱³, 池小斌¹, 吕立志¹, 陈永标¹^,^†(

)

¹　350025　福州，第九〇〇医院肝胆胰外科
²　350025　福州，福建医科大学福总临床医学院（第九〇〇医院）
³　350122　福州，福建医科大学

收稿日期:2025-10-27 出版日期:2026-06-10
通信作者: 陈永标
基金资助:
福建省自然科学基金面上项目(2023J011350); 福建医科大学启航基金(2024QH1404); 中国人民解放军联勤保障部队第九〇〇医院院内课题青年孵化项目(2023QN07)

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

Zhijian Chen¹, Jianda Yu², Zerun Lin², Honghuan Lin², Changyu Yi³, Xiaobin Chi¹, Lizhi Lyu¹, Yongbiao Chen¹^,^†()

¹ Department of Hepatobiliary and Pancreatic Surgery, the 900th Hospital, Fuzhou 350025, China
² School of Clinical Medicine of Fujian Medical University (The 900th Hospital), Fuzhou 350025, China
³ Fujian Medical University, Fuzhou 350122, China

Received:2025-10-27 Published:2026-06-10
Corresponding author: Yongbiao Chen

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引用本文：

陈志坚, 俞建达, 林泽润, 林宏焕, 易长昱, 池小斌, 吕立志, 陈永标. 基于术前影像肿瘤负荷评分的模型对单发肝细胞癌微血管侵犯的预测价值[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 379-385.

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/OL]. Chinese Journal of Hepatic Surgery(Electronic Edition), 2026, 15(03): 379-385.

目的

探讨基于术前影像肿瘤负荷评分（ITBS）的模型对单发肝细胞癌（HCC）微血管侵犯（MVI）的预测价值。

方法

回顾性分析2015年1月至2021年12月在第九〇〇医院接受手术治疗的213例单发HCC患者的临床资料。其中男148例，女65例，平均年龄（53±12）岁。采用ROC曲线评价ITBS预测MVI的性能，确定ITBS界值，依此分为高、低ITBS组。Logistic单因素和多因素回归分析MVI相关性，基于赤池信息量准则（AIC）构建列线图模型。采用校准曲线和ROC曲线验证模型的一致性及预测能力。采用决策曲线分析（DCA）评估预测模型在实际临床决策中应用价值。

结果

HCC患者ITBS值为1.28~18.03。ITBS预测HCC患者MVI发生的AUC为0.681（95%CI：0.592~0.769），灵敏度为0.647，特异度为0.710时，最佳临界值为5.9，据此临界值将患者分为低ITBS组（132例）和高ITBS组（81例）。Logistic多因素回归分析显示，男性、合并糖尿病、ALB低、Child-Pugh分级是HCC患者MVI的危险因素（OR=2.432，2.545，0.919，5.158；P<0.05）。基于独立危险因素和ITBS，按照AIC构建了列线图模型。ROC曲线分析显示，该列线图模型的AUC为0.767，灵敏度为0.608，特异度为0.809，提示预测效能良好。校准曲线显示，该列线图模型预测的发生概率和实际观察结果具有较好的一致性，外部验证也提示模型在预测方面的可靠性。DCA曲线表明，该列线图可作为有价值的预测工具预测单发HCC患者MVI的风险。

结论

ITBS对单发HCC患者MVI发生有一定的预测价值，基于性别、糖尿病、ALB、Child-Pugh分级和ITBS构建的列线图模型能够可靠、直观地反映单发HCC患者发生MVI的风险程度。

关键词: 癌，肝细胞, 微血管侵犯, 列线图, 影像肿瘤负荷评分

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

图1 ITBS预测单发HCC患者MVI的ROC曲线注：ITBS为影像肿瘤负荷评分

表1 低TBS组与高TBS组HCC患者基线资料比较

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

表1 低TBS组与高TBS组HCC患者基线资料比较

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

表2 Logistic回归分析单发HCC患者MVI的影响因素

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

表2 Logistic回归分析单发HCC患者MVI的影响因素

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

图2 预测肝癌患者MVI列线图模型构建及ROC曲线分析注：a为列线图模型；b为ROC曲线；ITBS为影像肿瘤负荷评分，MVI为微血管侵犯

图3 预测HCC患者MVI的列线图模型外部验证校准曲线和DCA 注：a、b为内、外部验证校准曲线，示列线图模型预测的发生概率和实际观察结果具有较好的一致性；c为DCA，None线（绿色水平线）为净收益始终为0，All线（蓝色斜线）示随着阈值增加，净收益逐渐下降；ITBS为影像肿瘤负荷评分，MVI为微血管侵犯；DCA为决策曲线分析

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

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