The application of radiomics and AI to molecular imaging for prostate cancer

Journal article

Tapper, William, Carneiro, G., Mikropoulos, C., Thomas, Spencer A., Evans, P. and Boussios, S. 2024. The application of radiomics and AI to molecular imaging for prostate cancer. Journal of Personalized Medicine. 14 (3), p. 287. https://doi.org/10.3390/jpm14030287
AuthorsTapper, William, Carneiro, G., Mikropoulos, C., Thomas, Spencer A., Evans, P. and Boussios, S.
AbstractMolecular imaging is a key tool in the diagnosis and treatment of prostate cancer (PCa). Magnetic Resonance (MR) plays a major role in this respect with nuclear medicine imaging, particularly, Prostate-Specific Membrane Antigen-based, (PSMA-based) positron emission tomography with computed tomography (PET/CT) also playing a major role of rapidly increasing importance. Another key technology finding growing application across medicine and specifically in molecular imaging is the use of machine learning (ML) and artificial intelligence (AI). Several authoritative reviews are available of the role of MR-based molecular imaging with a sparsity of reviews of the role of PET/CT. This review will focus on the use of AI for molecular imaging for PCa. It will aim to achieve two goals: firstly, to give the reader an introduction to the AI technologies available, and secondly, to provide an overview of AI applied to PET/CT in PCa. The clinical applications include diagnosis, staging, target volume definition for treatment planning, outcome prediction and outcome monitoring. ML and AL techniques discussed include radiomics, convolutional neural networks (CNN), generative adversarial networks (GAN) and training methods: supervised, unsupervised and semi-supervised learning.
KeywordsArtificial intelligence; Prostate cancer; Molecular Imaging; Pet/ct; Machine Learning; Radiomics
Year2024
JournalJournal of Personalized Medicine
Journal citation14 (3), p. 287
PublisherMDPI
ISSN2075-4426
Digital Object Identifier (DOI)https://doi.org/10.3390/jpm14030287
Official URLhttps://www.mdpi.com/2075-4426/14/3/287
Publication dates
Online07 Mar 2024
Print01 Mar 2024
Publication process dates
Accepted06 Mar 2024
Deposited13 Jun 2024
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Open
Output statusPublished
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