Clinical Applications of Artificial Intelligence in positron emission tomography of Lung Cancer
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Date
2022-01
Authors
Zukotynski, Katherine
Gaudet, Vincent C.
Uribe, Carlos F.
Chiam, Katarina
Benard, Francois
Gerbaudo, Victor
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
The ability of a computer to perform tasks normally requiring human intelligence or
artificial intelligence (AI) is not new. However, until recently, practical applications in medical
imaging were limited, especially in the clinic. With advances in theory, microelectronic circuits
and computer architecture as well as our ability to acquire and access large amounts of data, AI is
becoming increasingly ubiquitous in medical imaging. Of particular interest to our community,
radiomics tries to identify imaging features of specific pathology that can represent for example
the texture or shape of a region in the image. This is done based on a review of mathematical
patterns and pattern combinations. The difficulty is often finding sufficient data to span the
spectrum of disease heterogeneity since many features change with pathology as well as over time
and, among other issues, data acquisition is expensive. Although we are currently in the early days
of the practical application of AI to medical imaging, research is ongoing to integrate imaging,
molecular pathobiology, genetic make-up and clinical manifestations to classify patients into
subgroups for the purpose of precision medicine, or in other words, predicting a priori treatment
response and outcome. Lung cancer is a functionally and morphologically heterogeneous disease.
Positron emission tomography (PET) is an imaging technique with an important role in the
precision medicine of lung cancer patients that helps predict early response to therapy and guides
the selection of appropriate treatment. Although still in its infancy, early results suggest the use of
AI in PET of lung cancer has promise for the detection, segmentation and characterization of
disease as well as for outcome prediction.
Description
The final publication is available at Elsevier via https://doi.org/10.1016/j.cpet.2021.09.001. © 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
lung cancer, Positron Emission Tomography, pulmonary nodule, cancer diagnosis, targeted therapy, artificial intelligence