{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T09:54:41Z","timestamp":1791539681420,"version":"4.3.4"},"reference-count":40,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T00:00:00Z","timestamp":1773619200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Kaohsiung J of Med Scie"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>This study developed a non\u2010invasive model using PIVKA\u2010II 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\u2010Pugh A, BCLC stage A), comprising 43 MVI\u2010positive and 55 MVI\u2010negative 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\u2010II alone for MVI was analyzed. The MVI\u2010positive group showed significantly higher alpha\u2010fetoprotein \u2265\u2009400\u2009ng\/mL, PIVKA\u2010II levels, and aspartate aminotransferase levels. ROC curve analysis showed that PIVKA\u2010II alone had an AUC of 0.682 for diagnosing MVI, with a maximum Youden's index of 33.57, a cut\u2010off value of 741.5\u2009mAU\/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\u2010II, 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\u2010II alone. The model demonstrated good calibration and clinical utility. The non\u2010invasive predictive nomogram constructed by combining PIVKA\u2010II 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 \u201cA nomogram incorporating these factors achieved.......\u201d the value \u201c0.85\u201d is changed to \u201c0.81\u201d in this version.]<\/jats:p>","DOI":"10.1002\/kjm2.70203","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T04:02:41Z","timestamp":1773720161000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Construction of a Non\u2010Invasive Predictive Model Based on\n                    <scp>PIVKA<\/scp>\n                    \u2010\n                    <scp>II<\/scp>\n                    Combined With\n                    <scp>MRI<\/scp>\n                    Imaging Features for Evaluating Microvascular Invasion in Hepatocellular Carcinoma"],"prefix":"10.1002","volume":"42","author":[{"given":"Di","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Radiology Harbin 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