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Lee and Park: Application of Artificial Screening Intelligence in Lung Cancer
Review Article

J Korean Soc Radiol 2019;80(5):872-879.

Published online: 20 January 2019

DOI: https://doi.org/10.3348/jksr.2019.80.5.872

Application of Artificial Screening Intelligence in Lung Cancer

Sang Min Lee, MD∗, 1, Chang Min Park, MD2

1Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Korea

2Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea

∗Corresponding author Chang Min Park, MD Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea. Tel 82-2-2072-0367 Fax 82-2-743-7418 E-mail cmpark.morphius@gmail.com

Received 21 May 2019       Revised 5 August 2019       Accepted 6 August 2019

Copyright © 2019 The Korean Society of Radiology

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Lung cancer is a leading cause of deaths due to cancer, worldwide. At present, low-dose computed tomography (CT) is the only established screening method for reducing lung cancer mortality. However, several challenges must be overcome, to ensure the implementation of lung cancer screening, which include a large number of expected low-dose CT examinations and relative shortage of experienced radiologists for interpreting them. The use of artificial intelligence has garnered attention in this regard. A deep learning technique, which is a subclass of machine learning methods, involving the learning of data representations in an end-to-end manner, has already demonstrated outstanding performance in medical image analysis. Several studies are exploring the possibility of deep learning-based applications in medical domains, including radiology. In lung cancer screening, computer-aided detection, report generation, prediction of malignancy in the detected nodules, and prognosis prediction can be considered for the application of artificial intelligence. This article will cover the current status of deep learning approaches, their limitations, and their potential in lung cancer screening programs.

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