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ORIGINAL RESEARCH article

Front. Oncol.

Sec. Gastrointestinal Cancers: Hepato Pancreatic Biliary Cancers

Volume 15 - 2025 | doi: 10.3389/fonc.2025.1590655

This article is part of the Research TopicLiver Cancer Awareness Month 2024: Current Progress and Future Prospects on Advances in Primary Liver Cancer Investigation and TreatmentView all 21 articles

Exploring 2D and 3D Radiomic Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Novel Perspective on Tumor Heterogeneity

Provisionally accepted
Zexin  YinZexin YinMeilong  WuMeilong WuYouyao  LiYouyao LiZhike  LiZhike LiShiyun  BaoShiyun Bao*Liping  LiuLiping Liu*
  • Department of Hepatobiliary and Pancreatic Surgery, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China

The final, formatted version of the article will be published soon.

Objective This study aims to develop models for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients prior to surgery using two-dimensional (2D) and three-dimensional (3D) radiomics features from contrast-enhanced computed tomography (CT). The study compares the predictive performance of various models and explores the potential of radiomics to capture tumor spatial heterogeneity. Materials and Methods A total of 150 hepatocellular carcinoma (HCC) patients who underwent contrast-enhanced CT examination and curative resection were included in this study. 2D features from the largest cross-sectional slice, as well as 3D radiomic features, were extracted from the non-contrast (NC), arterial phase (AP), portal venous phase (PVP), and balanced phase (BP) images. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO) algorithm, and predictive models were constructed using logistic regression and XGBoost machine learning algorithms. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC). Results The 2D BP model (AUC = 0.801) and 3D PVP model (AUC = 0.876) showed superior performance among single-sequence models. The 2D multi-sequence model (AUC = 0.851) outperformed the 3D combined model (AUC = 0.811). Radiomics-based models outperformed clinical feature-based models, and combining radiomics scores with clinical features improved prediction accuracy. However, 3D models did not significantly outperform 2D models. Conclusion Both 2D and 3D radiomics models are effective for predicting MVI in HCC patients preoperatively. While the 3D model captures spatial heterogeneity, the 2D model excels at capturing local texture features. This study provides new insights into radiomics in HCC, contributing to its clinical application and standardization.

Keywords: Microvascular invasion, Intratumoral heterogeneity, Radiomics, two-dimensional and three-dimensional models, machine learning

Received: 10 Mar 2025; Accepted: 21 Jul 2025.

Copyright: © 2025 Yin, Wu, Li, Li, Bao and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Shiyun Bao, Department of Hepatobiliary and Pancreatic Surgery, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China
Liping Liu, Department of Hepatobiliary and Pancreatic Surgery, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, China

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