{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T14:58:51Z","timestamp":1790175531396,"version":"4.1.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T00:00:00Z","timestamp":1614816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Shanghai Shenkang Project","award":["No.16CR3024A"],"award-info":[{"award-number":["No.16CR3024A"]}]},{"DOI":"10.13039\/501100003399","name":"Shanghai Science and Technology Committee","doi-asserted-by":"crossref","award":["No.17441902700"],"award-info":[{"award-number":["No.17441902700"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003399","name":"Shanghai Science and Technology Committee","doi-asserted-by":"crossref","award":["No.18511102900, No.18511102901"],"award-info":[{"award-number":["No.18511102900, No.18511102901"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The Beijing Postdoctoral Research Foundation","award":["ZZ2019-88"],"award-info":[{"award-number":["ZZ2019-88"]}]},{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 81871508; No. 61572300; No. 61773246"],"award-info":[{"award-number":["No. 81871508; No. 61572300; No. 61773246"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Taishan Scholar Program of Shandong Province of China","award":["No. TSHW201502038"],"award-info":[{"award-number":["No. TSHW201502038"]}]},{"name":"Major Program of Shandong Province Natural Science Foundation","award":["No. ZR2018ZB0419"],"award-info":[{"award-number":["No. ZR2018ZB0419"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s00330-021-07794-0","type":"journal-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T19:02:52Z","timestamp":1614884572000},"page":"7162-7171","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Determining the invasiveness of ground-glass nodules using a 3D multi-task network"],"prefix":"10.1007","volume":"31","author":[{"given":"Ye","family":"Yu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Na","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinglong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjie","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yicheng","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huawei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianrong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6619-335X","authenticated-orcid":false,"given":"Jiejun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"7794_CR1","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1016\/j.rcl.2012.06.006","volume":"50","author":"P de Groot","year":"2012","unstructured":"de Groot P, Munden RF (2012) Lung cancer epidemiology, risk factors, and prevention. Radiol Clin North Am 50:863\u2013876","journal-title":"Radiol Clin North Am"},{"key":"7794_CR2","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21551","volume":"69","author":"RL Siegel","year":"2019","unstructured":"Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7\u201334","journal-title":"CA Cancer J Clin"},{"key":"7794_CR3","doi-asserted-by":"publisher","first-page":"533","DOI":"10.2214\/AJR.10.5813","volume":"196","author":"JM Goo","year":"2011","unstructured":"Goo JM, Park CM, Lee HJ (2011) Ground-glass nodules on chest CT as imaging biomarkers in the management of lung adenocarcinoma. AJR Am J Roentgenol 196:533\u2013543","journal-title":"AJR Am J Roentgenol"},{"key":"7794_CR4","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1148\/radiol.2462070712","volume":"246","author":"DM Hansell","year":"2008","unstructured":"Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697\u2013722","journal-title":"Radiology"},{"key":"7794_CR5","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1513\/pats.201107-042ST","volume":"8","author":"WD Travis","year":"2011","unstructured":"Travis WD, Brambilla E, Noguchi M et al (2011) International Association for the Study of Lung Cancer\/American Thoracic Society\/European Respiratory Society: international multidisciplinary classification of lung adenocarcinoma: executive summary. Proc Am Thorac Soc 8:381\u2013385","journal-title":"Proc Am Thorac Soc"},{"key":"7794_CR6","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1097\/JTO.0000000000000630","volume":"10","author":"WD Travis","year":"2015","unstructured":"Travis WD, Brambilla E, Nicholson AG et al (2015) The 2015 World Health Organization Classification of Lung Tumors: impact of genetic, clinical and radiologic advances since the 2004 classification. J Thorac Oncol 10:1243\u20131260","journal-title":"J Thorac Oncol"},{"key":"7794_CR7","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1183\/09031936.00027511","volume":"39","author":"PE Van Schil","year":"2012","unstructured":"Van Schil PE, Asamura H, Rusch VW et al (2012) Surgical implications of the new IASLC\/ATS\/ERS adenocarcinoma classification. Eur Respir J 39:478\u2013486","journal-title":"Eur Respir J"},{"key":"7794_CR8","doi-asserted-by":"publisher","first-page":"e278S","DOI":"10.1378\/chest.12-2359","volume":"143","author":"JA Howington","year":"2013","unstructured":"Howington JA, Blum MG, Chang AC, Balekian AA, Murthy SC (2013) Treatment of stage I and II non-small cell lung cancer: diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest 143:e278S\u2013e313S","journal-title":"Chest"},{"key":"7794_CR9","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/0003-4975(92)90428-7","volume":"54","author":"MJ Mack","year":"1992","unstructured":"Mack MJ, Aronoff RJ, Acuff TE, Douthit MB, Bowman RT, Ryan WH (1992) Present role of thoracoscopy in the diagnosis and treatment of diseases of the chest. Ann Thorac Surg 54:403\u2013408","journal-title":"Ann Thorac Surg"},{"key":"7794_CR10","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.3348\/kjr.2019.0155","volume":"20","author":"JW Lee","year":"2019","unstructured":"Lee JW, Park CH, Lee SM, Jeong M, Hur J (2019) Planting seeds into the lung: image-guided percutaneous localization to guide minimally invasive thoracic surgery. Korean J Radiol 20:1498\u20131514","journal-title":"Korean J Radiol"},{"key":"7794_CR11","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"D Shen","year":"2017","unstructured":"Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221\u2013248","journal-title":"Annu Rev Biomed Eng"},{"key":"7794_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"7794_CR13","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"Shin H-C, Roth HR, Gao M et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285\u20131298","journal-title":"IEEE Trans Med Imaging"},{"key":"7794_CR14","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.compbiomed.2017.04.006","volume":"89","author":"W Sun","year":"2017","unstructured":"Sun W, Zheng B, Qian W (2017) Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput Biol Med 89:530\u2013539","journal-title":"Comput Biol Med"},{"key":"7794_CR15","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1109\/TMI.2016.2536809","volume":"35","author":"AA Setio","year":"2016","unstructured":"Setio AA, Ciompi F, Litjens G et al (2016) Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 35:1160\u20131169","journal-title":"IEEE Trans Med Imaging"},{"key":"7794_CR16","doi-asserted-by":"publisher","first-page":"1558","DOI":"10.1109\/TBME.2016.2613502","volume":"64","author":"Q Dou","year":"2017","unstructured":"Dou Q, Chen H, Yu L, Qin J, Heng PA (2017) Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection. IEEE Trans Biomed Eng 64:1558\u20131567","journal-title":"IEEE Trans Biomed Eng"},{"key":"7794_CR17","doi-asserted-by":"publisher","first-page":"6881","DOI":"10.1158\/0008-5472.CAN-18-0696","volume":"78","author":"W Zhao","year":"2018","unstructured":"Zhao W, Yang J, Sun Y et al (2018) 3D deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res 78:6881\u20136889","journal-title":"Cancer Res"},{"key":"7794_CR18","doi-asserted-by":"publisher","first-page":"491","DOI":"10.21037\/qims.2018.06.03","volume":"8","author":"S Wang","year":"2018","unstructured":"Wang S, Wang R, Zhang S et al (2018) 3D convolutional neural network for differentiating pre-invasive lesions from invasive adenocarcinomas appearing as ground-glass nodules with diameters <\/=3 cm using HRCT. Quant Imaging Med Surg 8:491\u2013499","journal-title":"Quant Imaging Med Surg"},{"key":"7794_CR19","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1007\/s00330-019-06533-w","volume":"30","author":"J Gong","year":"2020","unstructured":"Gong J, Liu J, Hao W et al (2020) A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol 30:1847\u20131855","journal-title":"Eur Radiol"},{"key":"7794_CR20","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1007\/s00330-019-06344-z","volume":"30","author":"LL Qi","year":"2020","unstructured":"Qi LL, Wu BT, Tang W et al (2020) Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation. Eur Radiol 30:744\u2013755","journal-title":"Eur Radiol"},{"key":"7794_CR21","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. ICML 2015:448\u2013456 https:\/\/arxiv.org\/abs\/1502.03167"},{"key":"7794_CR22","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. IEEE Conf Comput Vis Pattern Recog (CVPR):2261\u20132269. https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"7794_CR23","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi S (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. Fourth Int Conf 3D Vis (3DV):565\u2013571. https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"7794_CR24","unstructured":"Paszke A, Gross S, Massa F et al (2019) PyTorch: an imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, pp 8024-8035. ArXiv abs\/1912:01703"},{"key":"7794_CR25","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.neunet.2017.06.003","volume":"93","author":"L Wang","year":"2017","unstructured":"Wang L, Yang Y, Min R, Chakradhar S (2017) Accelerating deep neural network training with inconsistent stochastic gradient descent. Neural Netw 93:219\u2013229","journal-title":"Neural Netw"},{"key":"7794_CR26","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1097\/JTO.0b013e318206a221","volume":"6","author":"WD Travis","year":"2011","unstructured":"Travis WD, Brambilla E, Noguchi M et al (2011) International association for the study of lung cancer\/american thoracic society\/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 6:244\u2013285","journal-title":"J Thorac Oncol"},{"key":"7794_CR27","doi-asserted-by":"publisher","first-page":"4967","DOI":"10.21037\/jtd.2017.11.04","volume":"9","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Tang J, Xu J, Cheng J, Wu H (2017) Analysis of pulmonary pure ground-glass nodule in enhanced dual energy CT imaging for predicting invasive adenocarcinoma: comparing with conventional thin-section CT imaging. J Thorac Dis 9:4967\u20134978","journal-title":"J Thorac Dis"},{"key":"7794_CR28","first-page":"136","volume":"65","author":"H Ding","year":"2017","unstructured":"Ding H, Shi J, Zhou X et al (2017) Value of CT characteristics in predicting invasiveness of adenocarcinoma presented as pulmonary ground-glass nodules. Thorac Cardiovasc Surg 65:136\u2013141","journal-title":"Thorac Cardiovasc Surg"},{"key":"7794_CR29","doi-asserted-by":"publisher","first-page":"4037","DOI":"10.1007\/s00330-017-4829-5","volume":"27","author":"F Wu","year":"2017","unstructured":"Wu F, Tian SP, Jin X et al (2017) CT and histopathologic characteristics of lung adenocarcinoma with pure ground-glass nodules 10 mm or less in diameter. Eur Radiol 27:4037\u20134043","journal-title":"Eur Radiol"},{"key":"7794_CR30","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1007\/s00330-015-4131-3","volume":"26","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Shen Y, Qiang JW, Ye JD, Zhang J, Zhao RY (2016) HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules. Eur Radiol 26:2921\u20132928","journal-title":"Eur Radiol"},{"key":"7794_CR31","doi-asserted-by":"publisher","first-page":"411.e7","DOI":"10.1016\/j.crad.2017.11.014","volume":"73","author":"X Yue","year":"2018","unstructured":"Yue X, Liu S, Liu S et al (2018) HRCT morphological characteristics distinguishing minimally invasive pulmonary adenocarcinoma from invasive pulmonary adenocarcinoma appearing as subsolid nodules with a diameter of <\/=3 cm. Clin Radiol 73:411.e7\u2013411.e15","journal-title":"Clin Radiol"},{"key":"7794_CR32","doi-asserted-by":"publisher","first-page":"W162","DOI":"10.2214\/AJR.18.21008","volume":"213","author":"Y Zhan","year":"2019","unstructured":"Zhan Y, Peng X, Shan F et al (2019) Attenuation and morphologic characteristics distinguishing a ground-glass nodule measuring 5-10 mm in diameter as invasive lung adenocarcinoma on thin-slice CT. AJR Am J Roentgenol 213:W162\u2013W170","journal-title":"AJR Am J Roentgenol"},{"key":"7794_CR33","doi-asserted-by":"publisher","first-page":"5383086","DOI":"10.1155\/2019\/5383086","volume":"2019","author":"MT Congedo","year":"2019","unstructured":"Congedo MT, Iezzi R, Nachira D et al (2019) Uniportal VATS coil-assisted resections for GGOs. J Oncol 2019:5383086. https:\/\/doi.org\/10.1155\/2019\/5383086","journal-title":"J Oncol"},{"key":"7794_CR34","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s00330-018-5530-z","volume":"29","author":"L Fan","year":"2019","unstructured":"Fan L, Fang M, Li Z et al (2019) Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule. Eur Radiol 29:889\u2013897","journal-title":"Eur Radiol"},{"key":"7794_CR35","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.ejrad.2019.06.010","volume":"117","author":"C Gao","year":"2019","unstructured":"Gao C, Xiang P, Ye J, Pang P, Wang S, Xu M (2019) Can texture features improve the differentiation of infiltrative lung adenocarcinoma appearing as ground glass nodules in contrast-enhanced CT? Eur J Radiol 117:126\u2013131","journal-title":"Eur J Radiol"},{"key":"7794_CR36","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1177\/0284185119826536","volume":"60","author":"Y Yang","year":"2019","unstructured":"Yang Y, Wang WW, Ren Y et al (2019) Computerized texture analysis predicts histological invasiveness within lung adenocarcinoma manifesting as pure ground-glass nodules. Acta Radiol 60:1258\u20131264","journal-title":"Acta Radiol"},{"key":"7794_CR37","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1148\/radiol.2018172361","volume":"288","author":"R Berenguer","year":"2018","unstructured":"Berenguer R, Pastor-Juan MDR, Canales-Vazquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407\u2013415","journal-title":"Radiology"},{"key":"7794_CR38","doi-asserted-by":"publisher","first-page":"W114","DOI":"10.2214\/AJR.19.21245","volume":"213","author":"Y Yang","year":"2019","unstructured":"Yang Y, Li K, Sun D et al (2019) Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as pure ground-glass nodules: differentiation using enhanced dual-source dual-energy CT. AJR Am J Roentgenol 213:W114\u2013W122","journal-title":"AJR Am J Roentgenol"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-021-07794-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-021-07794-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-021-07794-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T14:18:09Z","timestamp":1629469089000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-021-07794-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,4]]},"references-count":38,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["7794"],"URL":"https:\/\/doi.org\/10.1007\/s00330-021-07794-0","relation":{},"ISSN":["0938-7994","1432-1084"],"issn-type":[{"value":"0938-7994","type":"print"},{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,4]]},"assertion":[{"value":"30 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The scientific guarantor of this publication is Jiejun Cheng.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 retrospective\u2022 diagnostic study\u2022 performed at one institution","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}