Construction of a prediction model for predicting microvascular invasion in primary hepatocellular carcinoma
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摘要:
目的 探讨术前基于影像学和血清学特征构建的列线图模型对肝癌微血管浸润(MVI)的预测价值。 方法 回顾性分析2015年1月~2020年12月于中山市人民医院接受切除或肝移植的548例肝细胞癌(HCC)患者的临床资料，最终纳入315例肝癌MVI患者，年龄53.2±11.5岁，肿瘤最大直径3.7~7.0 cm。收集患者临床及影像学资料并进行分析，采取单因素与多因素Logistic分析，筛查出能预测MVI的独立风险因素，构建预测HCC中MVI的列线图模型，利用ROC曲线、校准曲线和决策曲线对模型进行评估。 结果 MVI (+)患者的中位生存时间为13月(95%CI：8.1~17.9)，1、3、5年无病生存率分别为50.6%、38.5%和30.9%(P < 0.05)；MVI (-)患者的中位生存时间为47月(95%CI：32.7~61.3)，1、3、5年无病生存率分别为77.9%、62.3%和38.8%(P < 0.05)。多因素Logistic回归分析显示，更大的肿瘤体积、突破肝外生长、缺乏或不完整假包膜、存在动脉期瘤周强化以及术前过高的球蛋白值是MVI (+)的独立危险因素(P < 0.05)。最终模型效能曲线下面积为0.895，95%CI为0.859-0.930，准确性为85.1%，敏感度为85.9%，特异性为84.1%。校准曲线显示预测概率与病理结果MVI (+)/MVI (-)概率有良好的一致性。决策曲线显示模型具有良好的临床应用价值。 结论 构建的列线图及预测模型能较好地术前预测MVI (+)的概率，可以根据MVI发生的风险调整HCC的治疗计划，以优化生存结果。 Abstract:Objective To investigate the value of a preoperative nomograph and prediction model based on imaging and serological characteristics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Methods Clinical data of 548 patients with HCC who underwent liver resection or liver transplantation from January 2015 to December 2020 in our Hospital were retrospectively included. A total of 315 patients with HCC (MVI+ or MVI-) with an average age of 53.2±11.5 years old and a maximum direct tumour of 3.7-7.0 cm were included. Clinical and imaging data were analyzed. Univariate and multivariate logistic analyses were used to screen out independent risk factors that could predict MVI, and a nomograph model was constructed to predict MVI in HCC, which was evaluated using a subject operating curve, calibration curve and decision curve. Results The median survival time of patients with MVI(+) was 13 months (95% CI: 8.1-17.9) while that of patients with MVI(-)was 47 months (95%CI: 32.7-61.3). The 1-year, 3-year and 5-year disease-free survival rates of patients with MVI(+) were 50.6%、38.5% and 30.9%, while that of patients with MVI(-) were 77.9%、62.3% and 38.8%, respectively. Multivariate logistic regression analysis showed that larger tumour size, extrahepatic growth, absence or incomplete pseudocapsule, presence of arterial peritumoral enhancement and high preoperative globulin value were independent risk factors for MVI(+). The final model efficacy were as follows: AUC=0.895, 95%CI: 0.859-0.930, accuracy: 85.1%, sensitivity: 85.9%, specificity: 84.1%. The calibration curve showed that the predicted probability was in good agreement with the MVI(+)/MVI(-) probability of the pathological results. Thus, the decision curve model displayed good clinical application value. Conclusion The constructed Nomograph and prediction model can better predict the probability of MVI(+) before surgery. It can aid in adjusting the treatment plan of HCC according to the risk of MVI to optimize the survival outcome. -
Key words:
- hepatocellular carcinoma /
- microvascular invasion /
- risk factors /
- nomograph
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图 2 根据多因素回归模型绘制列线图预测患者MVI(+)概率
Figure 2. The probability of MVI(+) in patients was predicted by drawing a line graph based on the multi- factor regression model.
图 3 融合模型及各单一变量对肝癌MVI(+)/ MVI(-)的预测效能的ROC曲线
融合模型AUC=0.895(0.859, 0.930)，曲线cut-off值上的敏感度为0.859，特异性为0.841，准确度为0.851.
Figure 3. ROC curves of the fusion model and the predictive efficacy of each single variable for liver cancer MVI(+)/MVI(-).
表 1 315例患者临床及影像基线特征
Table 1. Baseline clinical and imaging features of 315 patients[n(%)]
因素 总计(n=315) MVI(-)(n=145) MVI(+)(n=170) P 性別 0.579 女=0 42(13.3) 21(14.5) 21(12.4) 男=1 273(86.7) 124(85.5) 149(87.6) 年龄(岁, Mean±SD) 53.2±11.5 54.1±10.4 52.4±12.3 0.040 肿瘤最大直径(cm)* 4.9(3.7, 7.0) 3.7(2.6, 6.1) 5.9(3.6, 9) < 0.001 肿瘤边缘 0.665 光滑=0 64(20.3) 31(21.4) 33(19.4) 不光滑=1 251(79.7) 114(78.6) 137(80.6) 生长部位 0.001 肝内生长=0 202(64.1) 107(73.8) 95(55.9) 肝外生长=1 113(35.9) 38(26.2) 75(44.1) 累及肝段 0.001 一个肝段=0 127(40.3) 73(50.3) 54(31.8) 多个肝段=1 188(59.7) 72(49.7) 116(68.2) 肿瘤数量 0.968 单个=0 280(88.9) 129(89) 151(88.8) 多个=1 35(11.1) 16(11) 19(11.2) 假包膜 0.093 有=0 162(51.4) 82(56.6) 80(47.1) 无=1 153(48.6) 63(43.4) 90(52.9) 动脉期瘤周强化 < 0.001 无=0 128(40.6) 87(60) 41(24.1) 有=1 187(59.4) 58(40) 129(75.9) Stage分期 0.660 1=0 282(89.5) 131(90.3) 151(88.8) ≧2=1 33(10.5) 14(9.7) 19(11.2) 肝硬化 0.306 无=0 87(27.6) 36(24.8) 51(30) 有=1 228(72.4) 109(75.2) 119(70) 乙肝 0.091 无=0 59(18.7) 33(22.8) 26(15.3) 有=1 256(81.3) 112(77.2) 144(84.7) 术前谷丙转氨酶(U/L) 0.808 < 40=0 178(56.5) 83(57.2) 95(55.9) > 40=1 137(43.5) 62(42.8) 75(44.1) 术前谷草转氨酶(U/L) 0.046 < 35=0 146(46.3) 76(52.4) 70(41.2) > 35=1 169(53.7) 69(47.6) 100(58.8) AST/ALT 1(0.8, 1.3) 0.9(0.7, 1.2) 1(0.8, 1.4) 0.003 术前碱性磷酸酶(U/L) 0.215 < 150=0 284(90.2) 134(92.4) 150(88.2) > 150=1 31(9.8) 11(7.6) 20(11.8) 术前白蛋白(g/L) 0.293 < 55=0 95(30.2) 48(33.1) 47(27.6) > 55=1 220(69.8) 97(66.9) 123(72.4) 术前球蛋白(g/L) < 0.001 < 35=0 184(58.4) 125(86.2) 59(34.7) > 35=1 131(41.6) 20(13.8) 111(65.3) 白球比* 1.4(1.2, 1.6) 1.4(1.2, 1.6) 1.5(1.2, 1.7) 0.057 术前AFP(ng/mL) 0.150 < 20=0 121(38.4) 64(44.1) 57(33.5) 20~200=1 65(20.6) 28(19.3) 37(21.8) > 200=2 129(41.0) 53(36.6) 76(44.7) *以中位数(四分位数间距)表示.MVI: 微血管浸润. 
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表 2 临床影像实验室变量单因素和多因素Logistic回归分析
Table 2. Univariate and multivariate logistic regression analysis of clinical imaging laboratory variables
项目 单因素 多因素 OR (95%CI) P OR (95%CI) P 性別 女=0 Ref 男=1 1.202(0.627~2.302) 0.580 年龄 0.987(0.968~1.006) 0.183 肿瘤最大直径(cm) 1.231(1.138~1.332) < 0.001 1.237(1.116~1.372) < 0.001 肿瘤边缘 光滑=0 Ref 不光滑=1 1.129(0.652~1.956) 0.665 生长部位 肝内生长=0 Ref 肝外生长=1 2.223(1.378~3.586) 0.001 2.391(1.213~4.713) 0.012 累及肝段 一个肝段=0 Ref 多个肝段=1 2.178(1.377~3.445) 0.001 肿瘤数量 单个=0 Ref 多个=1 1.014(0.501~2.054) 0.968 假包膜 有=0 Ref 无1 1.464(0.938~2.286) 0.093 0.171(0.064~0.456) < 0.001 动脉期瘤周强化 无=0 Ref 有=1 4.72(2.91~7.655) < 0.001 20.618(7.504~56.65) < 0.001 Stage分期 1=0 Ref ≧2=1 1.177(0.568~2.441) 0.661 肝硬化 无=0 Ref 有=1 0.771(0.468~1.27) 0.307 乙肝 无=0 Ref 有=1 1.632(0.923~2.886) 0.092 术前谷丙转氨酶(U/L) < 40=0 Ref > 40=1 1.057(0.676~1.653) 0.808 术前谷草转氨酶(U/L) < 35=0 Ref > 35=1 1.573(1.007~2.46) 0.047 AST/ALT 1.621(1.065~2.469) 0.024 术前碱性磷酸酶(U/L) < 150=0 Ref > 150=1 1.624(0.751~3.514) 0.218 术前白蛋白(g/L) < 55=0 Ref > 55=1 1.295(0.8~2.098) 0.293 术前球蛋白(g/L) < 35=0 Ref > 35=1 11.758(6.664~20.749) < 0.001 17.077(8.531~34.185) < 0.001 白球比 0.889(0.695~1.136) 0.347 术前AFP(ng/mL) < 20=0 Ref 20~200=1 1.484(0.809~2.722) 0.203 > 200=2 1.61(0.976~2.656) 0.062
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表 3 融合模型及单一变量对肝癌MVI的预测效能
Table 3. Prediction efficiency of fusion model and single variable for MVI of liver cancer
因素 AUC 敏感度(%) 特异性(%) 准确性(%) 融合模型 0.895 (0.859~0.930) 85.9 84.1 85.1 肿瘤最大直径 0.679 (0.621~0.738) 65.3 62.8 64.1 生长部位 0.590 (0.527~0.652) 44.1 73.8 57.8 假包膜 0.547 (0.484~0.611) 52.9 56.6 54.6 动脉期瘤周强化 0.679 (0.619~0.740) 75.9 60.0 68.6 术前球蛋白 0.758 (0.703~0.812) 65.3 86.2 74.9 注：构建融合模型的变量包括肿瘤最大直径、生长部位、假包膜、动脉期瘤周强化以及术前球蛋白.
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