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Review | Open Access

Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies

Guang-Yu Dinga,b,1, Jie-Yi Shia,b,1, Xiao-Dong Wangc,1, Bo Yand, Xi-Yang Liuc, Qiang Gaoa,b,e,f( )
Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Shanghai 200032, China
Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Fudan University, Shanghai 200032, China
School of Computer Science and Technology, Xidian University, Xi'an 710126, China
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
Institute of Biomedical Sciences, Fudan University, Shanghai 200030, China
State Key Laboratory of Genetic Engineering, Fudan University, Shanghai 200433, China

1 These authors contributed equally to this work.

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Abstract

In recent years, significant advances have been achieved in liver cancer management with the development of artificial intelligence (AI). AI-based pathological analysis can extract crucial information from whole slide images to assist clinicians in all aspects from diagnosis to prognosis and molecular profiling. However, AI techniques have a “black box” nature, which means that interpretability is of utmost importance because it is key to ensuring the reliability of the methods and building trust among clinicians for actual clinical implementation. In this paper, we provide an overview of current technical advancements in the AI-based pathological analysis of liver cancer, and delve into the strategies used in recent studies to unravel the “black box” of AI's decision-making process.

Keywords

Artificial intelligenceLiver cancerPathologyInterpretative model

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iLIVER
Volume 3 Issue 1,
March 2024
DOI: 10.1016/j.iliver.2024.100082
Cite this article:
Ding G-Y, Shi J-Y, Wang X-D, et al. Artificial intelligence-based pathological analysis of liver cancer: Current advancements and interpretative strategies. iLIVER, 2024, 3(1). https://doi.org/10.1016/j.iliver.2024.100082

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Received: 02 December 2023
Revised: 25 December 2023
Accepted: 08 January 2024
Published: 08 February 2024
© 2024 The Authors. Tsinghua University Press.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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