Toward radiomics for assessment of response to systemic therapies in lung cancer

Shawn Sun; Florent L. Besson; Binsheng Zhao; Lawrence H. Schwartz; Laurent Dercle

doi:10.18632/oncotarget.27847

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Research Papers:

Toward radiomics for assessment of response to systemic therapies in lung cancer

Shawn Sun, Florent L. Besson, Binsheng Zhao, Lawrence H. Schwartz and Laurent Dercle _

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Oncotarget. 2020; 11:4677-4680. https://doi.org/10.18632/oncotarget.27847

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Abstract

Shawn Sun1, Florent L. Besson2, Binsheng Zhao1, Lawrence H. Schwartz1 and Laurent Dercle1

1 Department of Radiology, New York Presbyterian Hospital, Columbia University Medical Center, New York, New York, USA

2 Department of Biophysics, Nuclear Medicine-Molécular Imaging, Hôpitaux Universitaires Paris-Saclay, AP-HP, Université Paris Saclay/CEA/CNRS/Inserm/BioMaps, France

Correspondence to:

Laurent Dercle,

email:

[email protected]

Keywords: positron emission tomography; computed tomography; prognosis; lung cancer; immunotherapy

Received: November 28, 2020 Accepted: November 30, 2020 Published: December 22, 2020

Copyright: © 2020 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ABSTRACT

This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response was standardized and classically based on measuring changes in tumor lesion size. Recent breakthroughs in artificial intelligence pave the way for the use of radiomics in tumor response assessment. Such objective techniques would bring a remarkable transformation to conventional methods, which can be inherently subjective. Successful implementation of these technologies would allow for faster and more accurate predictions of treatment efficacy, which will be critical to the advancement of personalized medicine.

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Research Papers:

Toward radiomics for assessment of response to systemic therapies in lung cancer

Abstract