Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data s...

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Bibliographic Details
Published in:Seminars in cancer biology 2023-08, Vol.93, p.97-113
Main Authors: Chen, Mitchell, Copley, Susan J., Viola, Patrizia, Lu, Haonan, Aboagye, Eric O.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Diagnostic Imaging
Humans
Lung cancer
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - genetics
Machine Learning
Precision medicine
Precision Medicine - methods
Radiogenomics
Radiomics
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Summary:Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
ISSN:1044-579X
1096-3650
DOI:10.1016/j.semcancer.2023.05.004