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Artificial intelligence techniques in liver cancer.

Lulu Wang1,2, Mostafa Fatemi2, Azra Alizad3

  • 1Department of Engineering, School of Technology, Reykjavık University, Reykjavík, Iceland.

Frontiers in Oncology
|September 18, 2024
PubMed
Summary
This summary is machine-generated.

Artificial Intelligence (AI) enhances medical imaging for Hepatocellular Carcinoma (HCC) diagnosis and prognosis. AI-driven multi-modal systems integrate imaging with clinical data for better treatment prediction and patient selection in liver cancer care.

Keywords:
artificial intelligencedeep learningdiagnosishepatocellular carcinomaliver cancermachine learningmedical imagingprediction

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Hepatocellular Carcinoma (HCC) is a leading cause of cancer mortality globally.
  • Accurate diagnosis and prognosis of HCC are critical for effective treatment planning.
  • Medical imaging modalities like CT, MRI, and ultrasound are essential for HCC evaluation.

Purpose of the Study:

  • To provide an overview of AI-based medical imaging models for HCC diagnosis and prediction.
  • To explore the integration of AI with multi-modal data for enhanced HCC prognostication.
  • To discuss the clinical applications, challenges, and future directions of AI in HCC management.

Main Methods:

  • Review of recent advancements in AI-based medical imaging for HCC.
  • Integration of multi-modal data (imaging, EHR, clinical parameters) in AI prediction systems.
  • Focus on AI models for predicting HCC biological characteristics, prognosis, and treatment response.

Main Results:

  • AI-based multi-modal systems show potential for improving diagnostic accuracy and consistency in HCC.
  • These systems can predict treatment response (e.g., transarterial chemoembolization) and microvascular invasion.
  • AI assists in identifying optimal candidates for interventional therapy in HCC patients.

Conclusions:

  • AI-powered medical imaging and multi-modal systems represent a significant advancement in HCC diagnosis and management.
  • Clinical application of these AI techniques offers promise for personalized treatment strategies and improved patient outcomes.
  • Further research is needed to address challenges and optimize the integration of AI into routine clinical practice for HCC.