Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Gastroenterol. May 28, 2026; 32(20): 112559
Published online May 28, 2026. doi: 10.3748/wjg.v32.i20.112559
Published online May 28, 2026. doi: 10.3748/wjg.v32.i20.112559
Construction and efficacy validation of a multi-parametric contrast-enhanced ultrasound nomogram model for discriminating hepatic inflammatory lesions from malignancies
Si-Jie Mou, San-Mei Yu, Yan-Ni Xiang, Bo Tang, Department of Ultrasound, Taizhou First People’s Hospital, Taizhou 318020, Zhejiang Province, China
Author contributions: Mou SJ designed the study, collected and analyzed data, and wrote the manuscript; Mou SJ, Yu SM, Xiang YN and Tang B participated in the study’s conception and data collection; Mou SJ and Tang B participated in study design and provided guidance; all authors read and approved the final version.
Institutional review board statement: This study was approved by the Ethic Committee of Taizhou First People’s Hospital (Approval No. 2025-KY108-01).
Informed consent statement: Due to the retrospective and de-identified nature of this study, written informed consent was waived.
Conflict-of-interest statement: The authors have no financial relationships to disclose.
Data sharing statement: No additional data are available.
Corresponding author: Bo Tang, Department of Ultrasound, Taizhou First People’s Hospital, No. 218 Hengjie Road, Huangyan District, Taizhou 318020, Zhejiang Province, China.
Received: December 5, 2025
Revised: January 20, 2026
Accepted: February 25, 2026
Published online: May 28, 2026
Processing time: 166 Days and 0.5 Hours
Revised: January 20, 2026
Accepted: February 25, 2026
Published online: May 28, 2026
Processing time: 166 Days and 0.5 Hours
Core Tip
Core Tip: We developed and externally tested (within an independent test set) a multi-parametric contrast-enhanced ultrasound-based, SHapley Additive exPlanations-interpretable machine learning nomogram to differentiate hepatic inflammatory lesions from malignant tumors. Logistic regression showed the best overall generalization (area under the curve = 0.965; accuracy = 94.35%) and significantly outperformed alpha-fetoprotein and Model for End-Stage Liver Disease scores. Blood flow signals and wash-in time, as the key driving factors of model decision-making, can offer an intuitive and clinically relevant diagnostic reference basis.