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• Review •
Recent advances in MRI-based deep learning prediction of microvascular invasion in hepatocellular carcinoma
ZHAO Renqing  WU Qixin 

Cite this article as: ZHAO R Q, WU Q X. Recent advances in MRI-based deep learning prediction of microvascular invasion in hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(10): 191-195. DOI:10.12015/issn.1674-8034.2025.10.030.


[Abstract] Microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is a critical indicator for assessing tumor aggressiveness, postoperative recurrence risk, and prognostic stratification. Although postoperative pathology remains the gold standard for MVI diagnosis, its invasiveness and delayed results limit its utility in preoperative decision-making. In recent years, deep learning (DL) has demonstrated increasing potential for preoperative MVI prediction due to its capacity for automated feature learning. This review systematically summarizes recent advances in DL-based MVI prediction using MRI, outlining the methodological evolution and current limitations, with the aim of providing a reference for building generalizable intelligent prediction tools. Specifically, we highlight progress in several key areas, including the use of non-contrast MRI sequences and multi-sequence fusion, optimization of 2D and 3D convolutional architectures, multi-task learning frameworks, and integration of clinical and imaging features. Moreover, we identify four major challenges faced by current DL models in this domain: (1) limited generalizability due to lack of external validation; (2) missing imaging modalities affecting model adaptability; (3) insufficient interpretability restricting clinical applicability; (4) high computational and data requirements hindering deployment. To address these issues, we further discuss emerging trends such as lightweight network design, multi-center data collaboration, modality completion strategies, causal inference, and structured modeling, aiming to provide guidance for the development of efficient, robust, and clinically translatable predictive tools.
[Keywords] hepatocellular carcinoma；microvascular invasion；radiomics；deep learning；magnetic resonance imaging

ZHAO Renqing1   WU Qixin2*  

1 Department of Radiology, Affiliated Hospital of Youjiang University of Ethnic Medicine, Baise 533000, China

2 Department of Radiology, Chongzuo People's Hospital, Chongzuo 522000, China

Corresponding author: WU Q X, E-mail: 820830957@qq.com

Conflicts of interest   None.

Received  2025-03-04
Accepted  2025-07-07
DOI: 10.12015/issn.1674-8034.2025.10.030
Cite this article as: ZHAO R Q, WU Q X. Recent advances in MRI-based deep learning prediction of microvascular invasion in hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(10): 191-195. DOI:10.12015/issn.1674-8034.2025.10.030.

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