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Review
. 2023 Feb 21;4(2):100933.
doi: 10.1016/j.xcrm.2023.100933. Epub 2023 Feb 3.

Integration of artificial intelligence in lung cancer: Rise of the machine

Colton Ladbury  1 , Arya Amini  2 , Ameish Govindarajan  3 , Isa Mambetsariev  3 , Dan J Raz  4 , Erminia Massarelli  3 , Terence Williams  1 , Andrei Rodin  5 , Ravi Salgia  3
Affiliations
Review

Integration of artificial intelligence in lung cancer: Rise of the machine

Colton Ladbury et al. Cell Rep Med. .

Abstract

The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.

Keywords: artificial intelligence; big data; computer vision; deep learning; lung cancer; machine learning; natural language processing; neural network; radiomics.

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Conflict of interest statement

Declaration of interests The authors declare no competing interests.

Figures

Figure 1
Figure 1
Clinical AI workflow schema Simplified schema of workflow for implementation of AI in lung cancer clinic on the basis of artificial intelligence best practices.
Figure 2
Figure 2
Examples of radiographic changes that may benefit from being distinguished using artificial intelligence Representative non-contrast computed tomography slices showing patients who experienced (A) radiation pneumonitis, (B) immunotherapy pneumonitis, or (C) disease progression following cancer treatment are shown. The similarity of these images exemplify an area in which artificial intelligence might help providers distinguish subtle differences in imaging to make the correct diagnosis.
Figure 3
Figure 3
Examples of auto-contoured organs at risk for stage III lung cancer Normal organ image segmentation outlines (lung [orange], spinal cord [red], brachial plexus [yellow], esophagus [green], and trachea [blue]) on a computed tomographic scan generated using an in-house deep learning algorithm designed to streamline radiation treatment workflows.
See this image and copyright information in PMC

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