eISSN: 1897-4317
ISSN: 1895-5770
Gastroenterology Review/Przegląd Gastroenterologiczny
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without publication fees
3/2024
vol. 19
 
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abstract:
Review paper

The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review

Platon Dimopoulos
1
,
Admir Mulita
2, 3
,
Andreas Antzoulas
4
,
Sylvain Bodard
5
,
Vasileios Leivaditis
6
,
Ioanna Akrida
4
,
Nikolaos Benetatos
4
,
Konstantinos Katsanos
1
,
Christos-Nikolaos Anagnostopoulos
3
,
Francesk Mulita
4

  1. Department of Interventional Radiology, General University Hospital of Patras, Patras, Greece
  2. Medical Physics Department, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, Greece
  3. Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
  4. Department of Surgery, General University Hospital of Patras, Patras, Greece
  5. Department of Radiology, University of Paris Cite, Necker Hospital, Paris, France
  6. Department of Cardiothoracic and Vascular Surgery, Westpfalz Klinikum, Kaiserslautern, Germany
Gastroenterology Rev 2024; 19 (3): 221–230
DOI: https://doi.org/10.5114/pg.2024.143147
Online publish date: 2024/09/18
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Artificial intelligence (AI) and image processing are revolutionising the diagnosis and management of liver cancer. Recent advancements showcase AI’s ability to analyse medical imaging data, like computed tomography scans and magnetic resonance imaging, accurately detecting and classifying liver cancer lesions for early intervention. Predictive models aid prognosis estimation and recurrence pattern identification, facilitating personalised treatment planning. Image processing techniques enhance data analysis by precise segmentation of liver structures, fusion of information from multiple modalities, and feature extraction for informed decision-making. Despite progress, challenges persist, including the need for standardised datasets and regulatory considerations.
keywords:

liver cancer, hepatocellular carcinoma, image processing, artificial intelligence, machine learning, medical application
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