Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?

被引：47

作者：

Schreuder, Anton ^{[1
]}

Scholten, Ernst T. ^{[1
]}

van Ginneken, Bram ^{[1
,2
]}

Jacobs, Colin ^{[1
]}

机构：

[1] Radboudumc, Dept Radiol Nucl Med & Anat, Geert Grootepl 10, NL-6525 GA Nijmegen, Netherlands

[2] Fraunhofer MEVIS, Bremen, Germany

来源：

TRANSLATIONAL LUNG CANCER RESEARCH | 2021年 / 10卷 / 05期

关键词：

Lung cancer; artificial intelligence (AI); computed tomography (CT); pulmonary nodule; COMPUTER-AIDED DETECTION; LOW-DOSE CT; AUTOMATIC DETECTION; 2ND READER; BASE-LINE; PERFORMANCE; VALIDATION; PREDICTION; MORTALITY; VOLUME;

D O I：

10.21037/tlcr-2020-lcs-06

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.

引用

页码：2378 / 2388

页数：11

共 76 条

[1] Lung Cancer Incidence and Mortality with Extended Follow-up in the National Lung Screening Trial [J].

Aberle, Denise R. ;

Black, William C. ;

Chiles, Caroline ;

Church, Timothy R. ;

Gareen, Ilana F. ;

Gierada, David S. ;

Mahon, Irene ;

Miller, Eric A. ;

Pinsky, Paul F. ;

Sicks, JoRean D. .

JOURNAL OF THORACIC ONCOLOGY, 2019, 14 (10) :1732-1742

[2] Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening [J].

Aberle, Denise R. ;

Adams, Amanda M. ;

Berg, Christine D. ;

Black, William C. ;

Clapp, Jonathan D. ;

Fagerstrom, Richard M. ;

Gareen, Ilana F. ;

Gatsonis, Constantine ;

Marcus, Pamela M. ;

Sicks, JoRean D. .

NEW ENGLAND JOURNAL OF MEDICINE, 2011, 365 (05) :395-409

[3] Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices [J].

Abramoff, Michael D. ;

Lavin, Philip T. ;

Birch, Michele ;

Shah, Nilay ;

Folk, James C. .

NPJ DIGITAL MEDICINE, 2018, 1

[4]

AI for Radiology, IMPLEMENTATION GUIDE

[5]

American College of Radiology, 2019, LUNG SCREEN REP DAT

[6] Randomized Clinical Trials of Artificial Intelligence [J].

Angus, Derek C. .

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2020, 323 (11) :1043-1045

[7]

[Anonymous], 2018, Top ten causes of death [Internet]

[8]

[Anonymous], SCREEN PROT

[9] End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography [J].

Ardila, Diego ;

Kiraly, Atilla P. ;

Bharadwaj, Sujeeth ;

Choi, Bokyung ;

Reicher, Joshua J. ;

Peng, Lily ;

Tse, Daniel ;

Etemadi, Mozziyar ;

Ye, Wenxing ;

Corrado, Greg ;

Naidich, David P. ;

Shetty, Shravya .

NATURE MEDICINE, 2019, 25 (06) :954-+

[10] LUNGx Challenge for computerized lung nodule classification [J].

Armato, Samuel G., III ;

Drukker, Karen ;

Li, Feng ;

Hadjiiski, Lubomir ;

Tourassi, Georgia D. ;

Engelmann, Roger M. ;

Giger, Maryellen L. ;

Redmond, George ;

Farahani, Keyvan ;

Kirby, Justin S. ;

Clarke, Laurence P. .

JOURNAL OF MEDICAL IMAGING, 2016, 3 (04)

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