{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:27:19Z","timestamp":1761092839382,"version":"build-2065373602"},"reference-count":25,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/100000050","name":"National Heart, Lung, and Blood Institute","doi-asserted-by":"publisher","award":["HL133889-04"],"award-info":[{"award-number":["HL133889-04"]}],"id":[{"id":"10.13039\/100000050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2022,2]]},"DOI":"10.1016\/j.acra.2021.07.002","type":"journal-article","created":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T01:55:18Z","timestamp":1628301318000},"page":"S156-S164","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"S2","title":["Transparent Machine Learning Models to Diagnose Suspicious Thoracic Lesions Leveraging CT Guided Biopsy Data"],"prefix":"10.1016","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8928-3419","authenticated-orcid":false,"suffix":"Jr","given":"William D.","family":"Lindsay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Sachs","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James C.","family":"Gee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"suffix":"Jr","given":"Eduardo J.","family":"Mortani Barbosa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2021.07.002_bib0001","unstructured":"Cancer of the Lung and Bronchus - Cancer Stat Facts [Internet]. SEER. Accessed at: May 20, 2020. Available from: https:\/\/seer.cancer.gov\/statfacts\/html\/lungb.html"},{"issue":"21","key":"10.1016\/j.acra.2021.07.002_bib0002","doi-asserted-by":"crossref","first-page":"5007","DOI":"10.1002\/cncr.24545","article-title":"Randomized controlled trials of the efficacy of lung cancer screening by sputum cytology revisited: a combined mortality analysis from the Johns Hopkins lung project and the memorial sloan-kettering lung study","volume":"115","author":"VP","year":"2009","journal-title":"Cancer"},{"issue":"5","key":"10.1016\/j.acra.2021.07.002_bib0003","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","year":"2011","journal-title":"N Engl J Med"},{"issue":"1","key":"10.1016\/j.acra.2021.07.002_bib0004","doi-asserted-by":"crossref","first-page":"e27","DOI":"10.1016\/j.cllc.2016.07.006","article-title":"Costs of diagnostic assessment for lung cancer: a medicare claims analysis","volume":"18","author":"Lokhandwala","year":"2017","journal-title":"Clin Lung Cancer"},{"key":"10.1016\/j.acra.2021.07.002_bib0005","doi-asserted-by":"crossref","DOI":"10.1513\/AnnalsATS.201604-294OC","article-title":"Readiness for implementation of lung cancer screening: a national survey of VA pulmonologists","author":"Tukey","year":"2016","journal-title":"Ann Am Thorac Soc"},{"issue":"3","key":"10.1016\/j.acra.2021.07.002_bib0006","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1001\/jamainternmed.2016.9022","article-title":"Implementation of lung cancer screening in the veterans health administration","volume":"177","author":"Kinsinger","year":"2017","journal-title":"JAMA Intern Med"},{"issue":"45","key":"10.1016\/j.acra.2021.07.002_bib0007","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"issue":"8","key":"10.1016\/j.acra.2021.07.002_bib0008","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"10.1016\/j.acra.2021.07.002_bib0009","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1097\/JTO.0000000000000287","article-title":"Predicting lung cancer prior to surgical resection in patients with lung nodules","volume":"9","author":"Deppen","year":"2014","journal-title":"J Thorac Oncol"},{"key":"10.1016\/j.acra.2021.07.002_bib0010","first-page":"1","article-title":"Computer-aided diagnosis systems for lung cancer: challenges and methodologies","volume":"2013","author":"El-Baz","year":"2013","journal-title":"Int J Biomed Imaging"},{"issue":"2","key":"10.1016\/j.acra.2021.07.002_bib0011","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1148\/radiology.186.2.8421743","article-title":"Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory","volume":"186","author":"Gurney","year":"1993","journal-title":"Radiology"},{"issue":"2","key":"10.1016\/j.acra.2021.07.002_bib0012","first-page":"415","article-title":"Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis","volume":"186","author":"Gurney","year":"1993","journal-title":"Part II. Application. Radiology"},{"issue":"6","key":"10.1016\/j.acra.2021.07.002_bib0013","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.media.2010.05.005","article-title":"Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study","volume":"14","author":"van Ginneken","year":"2010","journal-title":"Med Image Anal"},{"issue":"12","key":"10.1016\/j.acra.2021.07.002_bib0014","doi-asserted-by":"crossref","first-page":"2120","DOI":"10.1016\/j.jtho.2016.07.002","article-title":"Predicting malignant nodules from screening CT scans","volume":"11","author":"Hawkins","year":"2016","journal-title":"J Thorac Oncol"},{"year":"2017","series-title":"Deep convolutional neural networks for lung cancer detection. tech. rep","author":"Chon","key":"10.1016\/j.acra.2021.07.002_bib0015"},{"issue":"10","key":"10.1016\/j.acra.2021.07.002_bib0016","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1007\/s11548-017-1605-6","article-title":"Pulmonary nodule classification with deep residual networks","volume":"12","author":"Nibali","year":"2017","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10.1016\/j.acra.2021.07.002_bib0017","article-title":"3D convolutional neural network for automatic detection of lung nodules in chest CT","volume":"10134","author":"Hamidiana","year":"2017","journal-title":"SPIE Med Imaging"},{"volume":"12","year":"2017","author":"Kang","key":"10.1016\/j.acra.2021.07.002_bib0018"},{"issue":"4","key":"10.1016\/j.acra.2021.07.002_bib0019","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1007\/s11548-017-1696-0","article-title":"Agile convolutional neural network for pulmonary nodule classification using CT images","volume":"13","author":"Zhao","year":"2018","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"10.1016\/j.acra.2021.07.002_bib0020","first-page":"286","article-title":"Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on","author":"van Ginneken","year":"2015","journal-title":"IEEE"},{"issue":"5","key":"10.1016\/j.acra.2021.07.002_bib0021","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.acra.2009.12.009","article-title":"Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images","volume":"17","author":"Chen","year":"2010","journal-title":"Acad Radiol"},{"issue":"1","key":"10.1016\/j.acra.2021.07.002_bib0022","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1067\/j.cpradiol.2017.04.003","article-title":"Limited utility of pulmonary nodule risk calculators for managing large nodules","volume":"47","author":"Hammer","year":"2018","journal-title":"Curr Probl Diagn Radiol"},{"issue":"2","key":"10.1016\/j.acra.2021.07.002_bib0023","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1118\/1.3528204","article-title":"The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans: The LIDC\/IDRI thoracic CT database of lung nodules","volume":"38","author":"Armato","year":"2011","journal-title":"Med Phys"},{"key":"10.1016\/j.acra.2021.07.002_bib0024","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors. Adv Neural Inf Process Syst 2017 Accessed at: May 20, 2020 4765\u20134774. Available from: http:\/\/papers.nips.cc\/paper\/7062-a-unified-approach-to-interpreting-model-predictions.pdf."},{"key":"10.1016\/j.acra.2021.07.002_bib0025","doi-asserted-by":"crossref","unstructured":"Ribeiro MT, Singh S, Guestrin C. Why should i trust you?\u201d: explaining the predictions of any classifier. 2016 Accessed at: October 4, 2020. Available from: http:\/\/arxiv.org\/abs\/1602.04938","DOI":"10.18653\/v1\/N16-3020"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S107663322100307X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S107663322100307X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T08:53:35Z","timestamp":1761036815000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S107663322100307X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2]]},"references-count":25,"alternative-id":["S107663322100307X"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2021.07.002","relation":{},"ISSN":["1076-6332"],"issn-type":[{"type":"print","value":"1076-6332"}],"subject":[],"published":{"date-parts":[[2022,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Transparent Machine Learning Models to Diagnose Suspicious Thoracic Lesions Leveraging CT Guided Biopsy Data","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2021.07.002","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}