Enhancing early detection of pancreatic cancer by integrating AI with advanced imaging techniques
1 Department of Fisheries and Aquaculture, J.S Tarkaa University, Makurdi, Nigeria.
2 College Department of Biology, University of Arkansas Little Rock, Arkansas, USA.
3 Department of Community Medicine and Public Health Abia state university teaching hospital, Aba, Nigeria.
4 Department of Obstetrics and Gynaecology, Abia State University Teaching Hospital, Aba, Nigeria.
5 Department of Health, Safety and Environment, Al Jaber and Partners, Doha, Qatar.
6 Department of International Relations, Federal University of Lafia, Nasarawa State, Nigeria.
Review Article
Magna Scientia Advanced Biology and Pharmacy, 2024, 12(02), 051–083
Article DOI: 10.30574/msabp.2024.12.2.0044
Publication history:
Received on 06 June 2024; revised on 19 July 2024; accepted on 22 July 2024
Abstract:
Pancreatic cancer remains one of the most lethal malignancies, with a five-year survival rate of less than 10%, primarily due to late-stage diagnosis and rapid disease progression. Early detection is critical for improving patient outcomes, yet current diagnostic methods lack the sensitivity and specificity needed for effective screening. This review explores the integration of advanced imaging techniques with artificial intelligence (AI) to enhance the early detection of pancreatic cancer. Emphasizing a biological approach, we examine the underlying molecular and cellular mechanisms that contribute to the pathogenesis of pancreatic cancer and how they manifest in imaging data.
Key imaging modalities, including high-resolution magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET), are evaluated for their efficacy in visualizing pancreatic abnormalities. AI algorithms, particularly machine learning and deep learning, are discussed in the context of their ability to analyze complex imaging datasets, identify subtle biomarkers, and predict disease onset with high accuracy.
We delve into the biological markers that AI algorithms can detect, such as changes in the tumor microenvironment, alterations in tissue architecture, and specific molecular signatures of pancreatic ductal adenocarcinoma (PDAC). Furthermore, the integration of AI with molecular imaging techniques, such as positron emission tomography-magnetic resonance imaging (PET-MRI) and optical coherence tomography (OCT), is explored to provide a multi-faceted approach to early diagnosis.
The review also highlights the potential of combining AI-driven imaging with liquid biopsies and genomics to create a comprehensive diagnostic framework. By leveraging the power of AI to interpret complex biological data, we propose a novel paradigm for the early detection of pancreatic cancer, aiming to improve screening protocols, enable timely therapeutic interventions, and ultimately enhance patient survival rates.
In supposition, the integration of AI with advanced imaging techniques holds significant promise for revolutionizing the early detection of pancreatic cancer. Continued research and clinical validation are essential to translate these technological advancements into routine clinical practice, offering hope for better prognostic outcomes in patients with this devastating disease.
Keywords:
Pancreatic cancer; Early detection; Advanced imaging techniques; Artificial intelligence (AI); Biological markers; Molecular imaging; Machine learning; Deep learning
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0