Construction of a Non-Invasive Predictive Model Based on PIVKA-II Combined With MRI Imaging Features for Evaluating Microvascular Invasion in Hepatocellular Carcinoma.

IF 3.1
The Kaohsiung journal of medical sciences Pub Date : 2026-10-01 Epub Date: 2026-03-16 DOI:10.1002/kjm2.70203
Di Gao, Rui-Qi Jin, Hong-Wei Wang
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Abstract

This study developed a non-invasive model using PIVKA-II and MRI features to predict microvascular invasion in hepatocellular carcinoma, providing a reliable tool for early risk assessment and personalized treatment planning. The study included 98 patients with pathologically confirmed HCC (Child-Pugh A, BCLC stage A), comprising 43 MVI-positive and 55 MVI-negative cases. Baseline clinical characteristics and MRI features were collected. Univariate analysis identified candidate variables associated with MVI, which were subsequently included in multivariate logistic regression analysis to identify independent influencing factors. A nomogram prediction model was developed based on the independent factors. The model's diagnostic performance, calibration, and clinical applicability were evaluated. The diagnostic value of PIVKA-II alone for MVI was analyzed. The MVI-positive group showed significantly higher alpha-fetoprotein ≥ 400 ng/mL, PIVKA-II levels, and aspartate aminotransferase levels. ROC curve analysis showed that PIVKA-II alone had an AUC of 0.682 for diagnosing MVI, with a maximum Youden's index of 33.57, a cut-off value of 741.5 mAU/mL, 37.21% sensitivity, 96.36% specificity. Tumor diameter, peritumoral abnormal enhancement, intratumoral arteries, and mean tumor ADC value showed significant differences between the two groups. Elevated PIVKA-II, intratumoral arteries, and mean tumor ADC value were independent influencing factors for MVI in HCC. A nomogram incorporating these factors achieved an AUC of 0.81, outperforming PIVKA-II alone. The model demonstrated good calibration and clinical utility. The non-invasive predictive nomogram constructed by combining PIVKA-II with MRI features demonstrates good predictive value and clinical applicability for assessing MVI in HCC patients. [Correction added on 25 March 2026, after first online publication: In the sentence "A nomogram incorporating these factors achieved......." the value "0.85" is changed to "0.81" in this version.].

基于PIVKA-II结合MRI影像学特征的肝细胞癌微血管侵袭无创预测模型构建
本研究利用PIVKA-II和MRI特征建立了一种无创模型来预测肝细胞癌的微血管侵袭,为早期风险评估和个性化治疗计划提供可靠的工具。该研究纳入了98例病理证实的HCC (Child-Pugh A, BCLC A期)患者,其中43例mvi阳性,55例mvi阴性。收集基线临床特征和MRI特征。单因素分析确定了与MVI相关的候选变量,随后将其纳入多因素logistic回归分析以确定独立影响因素。建立了基于独立因素的nomogram预测模型。评估模型的诊断性能、校准和临床适用性。分析单独PIVKA-II对MVI的诊断价值。mvi阳性组甲胎蛋白≥400 ng/mL、PIVKA-II水平和天冬氨酸转氨酶水平显著升高。ROC曲线分析显示,单独使用PIVKA-II诊断MVI的AUC为0.682,最大约登指数为33.57,临界值为741.5 mAU/mL,敏感性37.21%,特异性96.36%。两组间肿瘤直径、瘤周异常强化、瘤内动脉、肿瘤平均ADC值差异均有统计学意义。PIVKA-II、瘤内动脉和肿瘤平均ADC值升高是HCC中MVI的独立影响因素。结合这些因素的nomogram AUC为0.85,优于单独使用PIVKA-II。该模型具有良好的校正效果和临床应用价值。PIVKA-II结合MRI特征构建的无创预测图对评估HCC患者的MVI具有良好的预测价值和临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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