ArticleThe Kaohsiung journal of medical sciences2026
Construction of a Non-Invasive Predictive Model Based on PIVKA-II Combined With MRI Imaging Features for Evaluating Microvascular Invasion in Hepatocellular Carcinoma.
Article in The Kaohsiung journal of medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.].
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