{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:24:21Z","timestamp":1769574261062,"version":"3.49.0"},"reference-count":30,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011913","name":"Kaohsiung Veterans General Hospital","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011913","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","clinicalradiologyonline.net","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Clinical Radiology"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1016\/j.crad.2019.08.005","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T07:44:03Z","timestamp":1568187843000},"page":"38-45","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":62,"title":["Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice"],"prefix":"10.1016","volume":"75","author":[{"given":"C.-H.","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Y.-C.","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.-T.","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.","family":"Garcia-Castro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Alberich-Bayarri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F.-Z.","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.crad.2019.08.005_bib1","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/S0033-8389(22)01116-2","article-title":"Factors affecting perception of pulmonary lesions","volume":"21","author":"Brogdon","year":"1983","journal-title":"Radiol Clin N Am"},{"issue":"3","key":"10.1016\/j.crad.2019.08.005_bib2","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1053\/j.ro.2015.01.008","article-title":"Pitfalls in chest radiographic interpretation: blind spots","volume":"50","author":"de Groot","year":"2015","journal-title":"Sem Roentgenol"},{"issue":"5","key":"10.1016\/j.crad.2019.08.005_bib3","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1056\/NEJMoa1102873","article-title":"Reduced lung-cancer mortality with low-dose computed tomographic screening","volume":"365","author":"Aberle","year":"2011","journal-title":"N Engl J Med"},{"issue":"1","key":"10.1016\/j.crad.2019.08.005_bib4","doi-asserted-by":"crossref","first-page":"15790","DOI":"10.1038\/s41598-017-16042-9","article-title":"Semiquantative visual assessment of sub-solid pulmonary nodules \u22643 cm in differentiation of lung adenocarcinoma spectrum","volume":"7","author":"Wu","year":"2017","journal-title":"Sci Rep"},{"issue":"10","key":"10.1016\/j.crad.2019.08.005_bib5","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1016\/j.acra.2018.01.012","article-title":"Modified Lung-RADS improves performance of screening LDCT in a population with high prevalence of non-smoking-related lung cancer","volume":"25","author":"Hsu","year":"2018","journal-title":"Acad Radiol"},{"issue":"5","key":"10.1016\/j.crad.2019.08.005_bib6","doi-asserted-by":"crossref","first-page":"e45","DOI":"10.1016\/j.cllc.2016.03.004","article-title":"Assessment of selection criteria for low-dose lung screening CT among Asian ethnic groups in Taiwan: from mass screening to specific risk-based screening for non-smoker lung cancer","volume":"17","author":"Wu","year":"2016","journal-title":"Clin Lung Cancer"},{"issue":"2","key":"10.1016\/j.crad.2019.08.005_bib7","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1148\/radiol.2522081895","article-title":"Workload of radiologists in United States in 2006\u20132007 and trends since 1991\u20131992","volume":"252","author":"Bhargavan","year":"2009","journal-title":"Radiology"},{"issue":"10","key":"10.1016\/j.crad.2019.08.005_bib8","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1016\/j.jacr.2013.02.016","article-title":"Analysis of radiologists' imaging workload trends by place of service","volume":"10","author":"Levin","year":"2013","journal-title":"J Am Coll Radiol"},{"issue":"1","key":"10.1016\/j.crad.2019.08.005_bib9","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s13244-016-0534-1","article-title":"Error and discrepancy in radiology: inevitable or avoidable?","volume":"8","author":"Brady","year":"2017","journal-title":"Insights Imaging"},{"issue":"6","key":"10.1016\/j.crad.2019.08.005_bib10","first-page":"485","article-title":"Radiologic errors in patients with lung cancer","volume":"134","author":"Forrest","year":"1981","journal-title":"West J Med"},{"issue":"5","key":"10.1016\/j.crad.2019.08.005_bib11","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"10.1016\/j.crad.2019.08.005_bib12","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/TMI.2018.2806086","article-title":"Fully convolutional architectures for multiclass segmentation in chest radiographs","volume":"37","author":"Novikov","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"issue":"5","key":"10.1016\/j.crad.2019.08.005_bib13","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1097\/RLI.0000000000000341","article-title":"Training and validating a deep convolutional neural network for computer-aided detection and classification of abnormalities on frontal chest radiographs","volume":"52","author":"Cicero","year":"2017","journal-title":"Invest Radiol"},{"issue":"2","key":"10.1016\/j.crad.2019.08.005_bib14","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.crad.2019.08.005_bib15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1186\/1472-6947-13-90","article-title":"Automated identification of pneumonia in chest radiograph reports in critically ill patients","volume":"13","author":"Liu","year":"2013","journal-title":"BMC Med Inform Decis Making"},{"key":"10.1016\/j.crad.2019.08.005_bib16","first-page":"2015","article-title":"Computer-aided classification of lung nodules on computed tomography images via deep learning technique","volume":"8","author":"Hua","year":"2015","journal-title":"OncoTargets Ther"},{"key":"10.1016\/j.crad.2019.08.005_bib17","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","article-title":"Deep convolutional neural networks for multi-modality isointense infant brain image segmentation","volume":"108","author":"Zhang","year":"2015","journal-title":"NeuroImage"},{"key":"10.1016\/j.crad.2019.08.005_bib18","series-title":"Biomedical texture analysis","first-page":"247","article-title":"Chapter 9: deep learning techniques on texture analysis of chest and breast images","author":"Cheng","year":"2017"},{"issue":"1","key":"10.1016\/j.crad.2019.08.005_bib19","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1148\/radiol.2018180237","article-title":"Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs","volume":"290","author":"Nam","year":"2019","journal-title":"Radiology"},{"issue":"8","key":"10.1016\/j.crad.2019.08.005_bib20","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1136\/thoraxjnl-2015-207221","article-title":"The British Thoracic Society guidelines on the investigation and management of pulmonary nodules","volume":"70","author":"Baldwin","year":"2015","journal-title":"Thorax"},{"issue":"11","key":"10.1016\/j.crad.2019.08.005_bib21","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002686","article-title":"Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists","volume":"15","author":"Rajpurkar","year":"2018","journal-title":"PLoS Med"},{"key":"10.1016\/j.crad.2019.08.005_bib22","series-title":"IEEE conference on computer vision and pattern recognition (CVPR), Honolulu, HI","first-page":"3462","article-title":"ChestX-Ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","author":"Wang","year":"2017"},{"issue":"1\u20132","key":"10.1016\/j.crad.2019.08.005_bib23","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/S0167-5877(00)00115-X","article-title":"Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests","volume":"45","author":"Greiner","year":"2000","journal-title":"Prev Vet Med"},{"issue":"3","key":"10.1016\/j.crad.2019.08.005_bib24","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"issue":"4","key":"10.1016\/j.crad.2019.08.005_bib25","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","article-title":"Deep learning in medical imaging: general overview","volume":"18","author":"Lee","year":"2017","journal-title":"Korea J Radiol"},{"issue":"2","key":"10.1016\/j.crad.2019.08.005_bib26","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1148\/radiol.2018171820","article-title":"Current applications and future impact of machine learning in radiology","volume":"288","author":"Choy","year":"2018","journal-title":"Radiology"},{"issue":"1","key":"10.1016\/j.crad.2019.08.005_bib27","doi-asserted-by":"crossref","first-page":"4165","DOI":"10.1038\/s41598-018-22437-z","article-title":"Detecting and classifying lesions in mammograms with deep learning","volume":"8","author":"Ribli","year":"2018","journal-title":"Sci Rep"},{"issue":"3","key":"10.1016\/j.crad.2019.08.005_bib28","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s13244-018-0599-0","article-title":"Added value of double reading in diagnostic radiology, a systematic review","volume":"9","author":"Geijer","year":"2018","journal-title":"Insights Imaging"},{"issue":"9","key":"10.1016\/j.crad.2019.08.005_bib29","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1038\/sj.bjc.6601356","article-title":"Comparison of standard and double reading and computer-aided detection (CAD) of interval cancers at prior negative screening mammograms: blind review","volume":"89","author":"Ciatto","year":"2003","journal-title":"Br J Cancer"},{"issue":"2","key":"10.1016\/j.crad.2019.08.005_bib30","doi-asserted-by":"crossref","first-page":"118","DOI":"10.5152\/dir.2016.16187","article-title":"Missed lung cancer: when, where, and why?","volume":"23","author":"del Ciello","year":"2017","journal-title":"Diagn Interv Radiol"}],"container-title":["Clinical Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0009926019303782?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0009926019303782?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,14]],"date-time":"2025-09-14T16:40:45Z","timestamp":1757868045000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0009926019303782"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["S0009926019303782"],"URL":"https:\/\/doi.org\/10.1016\/j.crad.2019.08.005","relation":{},"ISSN":["0009-9260"],"issn-type":[{"value":"0009-9260","type":"print"}],"subject":[],"published":{"date-parts":[[2020,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice","name":"articletitle","label":"Article Title"},{"value":"Clinical Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.crad.2019.08.005","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}