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

[21] This Week in the Journal [J].

de Koning, H. J. ;

van der Aalst, C. M. ;

de Jong, P. A. ;

Scholten, E. T. ;

Nackaerts, K. ;

Heuvelmans, M. A. ;

Lammers, J. -W. J. ;

Weenink, C. ;

Yousaf-Khan, U. ;

Horeweg, N. ;

van't Westeinde, S. ;

Prokop, M. ;

Mali, W. P. ;

Hoesein, F. A. A. Mohamed ;

van Ooijen, P. M. A. ;

Aerts, J. G. J. V. ;

den Bakker, M. A. ;

Thunnissen, E. ;

Verschakelen, J. ;

Vliegenthart, R. ;

Walter, J. E. ;

ten Haaf, K. ;

Groen, H. J. M. ;

Oudkerk, M. .

NEW ENGLAND JOURNAL OF MEDICINE, 2020, 382 (06) :503-513

[22] Use of Volumetry for Lung Nodule Management: Theory and Practice [J].

Devaraj, Anand ;

van Ginneken, Bram ;

Nair, Arjun ;

Baldwin, David .

RADIOLOGY, 2017, 284 (03) :630-644

[23] Pulmonary nodules: Interscan variability of semiautomated volume measurements with multisection CT-influence of inspiration level, nodule size, and segmentation performance [J].

Gietema, Hester A. ;

Schaefer-Prokop, Cornelia M. ;

Mali, Willem P. T. M. ;

Groenewegen, Gerard ;

Prokop, Mathias .

RADIOLOGY, 2007, 245 (03) :888-894

[24] Knowledge-Based Analysis for Mortality Prediction From CT Images [J].

Guo, Hengtao ;

Kruger, Uwe ;

Wang, Ge ;

Kalra, Mannudeep K. ;

Yan, Pingkun .

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2020, 24 (02) :457-464

[25] Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening [J].

Han, Daiwei ;

Heuvelmans, Marjolein A. ;

Oudkerk, Matthijs .

TRANSLATIONAL LUNG CANCER RESEARCH, 2017, 6 (01) :52-61

[26] CT Screening for Lung Cancer: Part-Solid Nodules in Baseline and Annual Repeat Rounds [J].

Henschke, Claudia I. ;

Yip, Rowena ;

Smith, James P. ;

Wolf, Andrea S. ;

Flores, Raja M. ;

Liang, Mingzhu ;

Salvatore, Mary M. ;

Liu, Ying ;

Xu, Dong Ming ;

Yankelevitz, David F. .

AMERICAN JOURNAL OF ROENTGENOLOGY, 2016, 207 (06) :1176-1184

[27] Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening [J].

Heuvelmans, Marjolein A. ;

Walter, Joan E. ;

Vliegenthart, Rozemarijn ;

van Ooijen, Peter M. A. ;

De Bock, Geertruida H. ;

de Koning, Harry J. ;

Oudkerk, Matthijs .

THORAX, 2018, 73 (08) :779-781

[28] Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study [J].

Hosny, Ahmed ;

Parmar, Chintan ;

Coroller, Thibaud P. ;

Grossmann, Patrick ;

Zeleznik, Roman ;

Kumar, Avnish ;

Bussink, Johan ;

Gillies, Robert J. ;

Mak, Raymond H. ;

Aerts, Hugo J. W. L. .

PLOS MEDICINE, 2018, 15 (11)

[29] Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method [J].

Huang, Peng ;

Lin, Cheng T. ;

Li, Yuliang ;

Tammemagi, Martin C. ;

Brock, Malcolm V. ;

Atkar-Khattra, Sukhinder ;

Xu, Yanxun ;

Hu, Ping ;

Mayo, John R. ;

Schmidt, Heidi ;

Gingras, Michel ;

Pasian, Sergio ;

Stewart, Lori ;

Tsai, Scott ;

Seely, Jean M. ;

Manos, Daria ;

Burrowes, Paul ;

Bhatia, Rick ;

Tsao, Ming-Sound ;

Lam, Stephen .

LANCET DIGITAL HEALTH, 2019, 1 (07) :E353-E362

[30] Spectrum of Subsolid Pulmonary Nodules and Overdiagnosis [J].

Hutchinson, Barry D. ;

Moreira, Andre L. ;

Ko, Jane P. .

SEMINARS IN ROENTGENOLOGY, 2017, 52 (03) :143-155

← 1 2 3 4 5 6 7 8 →