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Updated: Sep 13, 2025

A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma

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Published on: September 30, 2021

Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and

Wei Ding¹, Jinxing Zhang², Zhicheng Jin³

¹Department of Interventional Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, No. 299 Qingyang Road, Wuxi, 214023, China.

Discover Oncology

|July 28, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) pathomics offers new ways to analyze liver cancer (hepatocellular carcinoma) images for better diagnosis and treatment. Challenges like data issues and lack of standards must be overcome for widespread clinical use.

Keywords:

Artificial intelligence Challenge Diagnosis Hepatocellular carcinoma Pathological foundation model Pathomics Prognosis

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Last Updated: Sep 13, 2025

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A Biomimetic Model for Liver Cancer to Study Tumor-Stroma Interactions in a 3D Environment with Tunable Bio-Physical Properties

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Published on: August 7, 2020

Area of Science:

Oncology
Digital Pathology
Artificial Intelligence

Background:

Hepatocellular carcinoma (HCC) is a deadly cancer with complex causes and diverse characteristics, making diagnosis and treatment difficult.
Artificial intelligence (AI) is emerging as a powerful tool for precision oncology, offering new approaches to cancer care.
Digital pathology and AI-driven pathomics can extract quantitative data from histopathological images for HCC analysis.

Purpose of the Study:

To explore the potential of AI-based pathomics in improving the diagnosis, treatment, and prognostic prediction of hepatocellular carcinoma (HCC).
To discuss the role of pathological foundation models in developing specialized pathomics tools for HCC.
To identify challenges and future directions for the clinical implementation of pathomics in HCC management.

Main Methods:

Utilizing AI to analyze whole-slide histopathological images for quantitative data extraction (pathomics).
Leveraging emerging pathological foundation models to build specialized pathomics models for HCC.
Reviewing current research and identifying barriers to clinical adoption.

Main Results:

Pathomics enables quantitative analysis of histopathological images, holding promise for HCC diagnosis, treatment, and prognosis.
Foundation models provide a framework for developing tailored pathomics solutions for HCC.
Significant challenges impede clinical implementation, including data heterogeneity, interpretability, ethics, regulations, and lack of standardization.

Conclusions:

AI-powered pathomics presents a transformative potential for personalized HCC treatment.
Overcoming challenges related to data, interpretability, ethics, regulation, and standardization is crucial for clinical translation.
Future research should focus on multi-center studies, multi-modal data integration, and establishing industry standards for pathomics in HCC.