{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T21:31:02Z","timestamp":1772659862664,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0710800"],"award-info":[{"award-number":["2022YFF0710800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF0710802"],"award-info":[{"award-number":["2022YFF0710802"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071311"],"award-info":[{"award-number":["62071311"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Stable Support Plan for Colleges and Universities in Shenzhen of China","award":["SZWD2021010"],"award-info":[{"award-number":["SZWD2021010"]}]},{"name":"Special Program for Key Fields of Colleges and Universities in Guangdong Province (Biomedicine and Health) of China","award":["2021ZDZX2008"],"award-info":[{"award-number":["2021ZDZX2008"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s11517-024-03016-z","type":"journal-article","created":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T09:03:25Z","timestamp":1708074205000},"page":"1733-1749","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images"],"prefix":"10.1007","volume":"62","author":[{"given":"Xingguang","family":"Deng","sequence":"first","affiliation":[]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yingjian","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Shicong","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Nanrong","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Jiaxuan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Haseeb","family":"Hassan","sequence":"additional","affiliation":[]},{"given":"Ziran","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Xiaoqiang","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Yingwei","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Rongchang","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Kang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"3016_CR1","doi-asserted-by":"crossref","unstructured":"Singh D, Agusti A, Anzueto A, Barnes PJ, Bourbeau J, Celli BR, Criner GJ, Frith P, Halpin DM, Han M, et al (2019) Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: the gold science committee report 2019. Eur Respir J 53(5)","DOI":"10.1183\/13993003.00164-2019"},{"key":"3016_CR2","doi-asserted-by":"crossref","unstructured":"Naghavi M, Abajobir AA, Abbafati C, Abbas KM, Abd-Allah F, Abera SF, Aboyans V, Adetokunboh O, Afshin A, Agrawal A, et al (2017) Global, regional, and national age-sex specific mortality for 264 causes of death, 1980\u20132016: a systematic analysis for the global burden of disease study 2016. The Lancet 390(10100):1151\u20131210","DOI":"10.1016\/S0140-6736(17)32152-9"},{"key":"3016_CR3","doi-asserted-by":"publisher","unstructured":"Fabbri L, Pauwels R, Hurd S (2007) Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: gold executive summary updated 2003. COPD: J Chron Obstruct Pulmon Dis 1(1):105\u2013141. https:\/\/doi.org\/10.1081\/copd-120030163","DOI":"10.1081\/copd-120030163"},{"key":"3016_CR4","doi-asserted-by":"crossref","unstructured":"Fortis S, Comellas A, Make BJ, Hersh CP, Bodduluri S, Georgopoulos D, Kim V, Criner GJ, Dransfield MT, Bhatt SP (2019) Combined forced expiratory volume in 1 second and forced vital capacity bronchodilator response, exacerbations, and mortality in chronic obstructive pulmonary disease. Ann Am Thorac Soc 16(7):826\u2013835","DOI":"10.1513\/AnnalsATS.201809-601OC"},{"key":"3016_CR5","doi-asserted-by":"crossref","unstructured":"Flesch JD, Dine CJ (2012) Lung volumes: measurement, clinical use, and coding. Chest 142(2):506\u2013510","DOI":"10.1378\/chest.11-2964"},{"key":"3016_CR6","doi-asserted-by":"crossref","unstructured":"Fan L, Xia Y, Guan Y, Zhang T-f, Liu S-y (2014) Characteristic features of pulmonary function test, CT volume analysis and MR perfusion imaging in COPD patients with different HRCT phenotypes. Clin Respir J 8(1):45\u201354","DOI":"10.1111\/crj.12033"},{"issue":"1","key":"3016_CR7","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1148\/radiol.2015141579","volume":"277","author":"DA Lynch","year":"2015","unstructured":"Lynch DA, Austin JH, Hogg JC, Grenier PA, Kauczor H-U, Bankier AA, Barr RG, Colby TV, Galvin JR, Gevenois PA et al (2015) Ct-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner Society. Radiology 277(1):192\u2013205","journal-title":"Radiology"},{"key":"3016_CR8","doi-asserted-by":"crossref","unstructured":"Lynch DA (2014) Progress in imaging COPD, 2004-2014. Chronic Obstr Pulm Dis J COPD Found 1(1):73","DOI":"10.15326\/jcopdf.1.1.2014.0125"},{"key":"3016_CR9","doi-asserted-by":"crossref","unstructured":"Schroeder JD, McKenzie AS, Zach JA, Wilson CG, Curran-Everett D, Stinson DS, Newell Jr JD, Lynch DA (2013)\u00a0Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease. AJR Am J Roentgenol 201(3):460","DOI":"10.2214\/AJR.12.10102"},{"key":"3016_CR10","doi-asserted-by":"crossref","unstructured":"Bhatt SP, Washko GR, Hoffman EA, Newell\u00a0Jr JD, Bodduluri S, Diaz AA, Galban CJ, Silverman EK, San Jos\u00e9\u00a0Est\u00e9par R, Lynch DA (2019) Imaging advances in chronic obstructive pulmonary disease. insights from the genetic epidemiology of chronic obstructive pulmonary disease (copdgene) study. Am J Respir Crit Care Med 199(3):286\u2013301","DOI":"10.1164\/rccm.201807-1351SO"},{"key":"3016_CR11","doi-asserted-by":"crossref","unstructured":"Subramanian DR, Gupta S, Burggraf D, Vom\u00a0Silberberg SJ, Heimbeck I, Heiss-Neumann MS, Haeussinger K, Newby C, Hargadon B, Raj V, et al (2016) Emphysema-and airway-dominant COPD phenotypes defined by standardised quantitative computed tomography. Eur Respir J 48(1):92\u2013103","DOI":"10.1183\/13993003.01878-2015"},{"key":"3016_CR12","doi-asserted-by":"crossref","unstructured":"McDonough JE, Yuan R, Suzuki M, Seyednejad N, Elliott WM, Sanchez PG, Wright AC, Gefter WB, Litzky L, Coxson HO, et al (2011) Small-airway obstruction and emphysema in chronic obstructive pulmonary disease. N Engl J Med 365(17):1567\u20131575","DOI":"10.1056\/NEJMoa1106955"},{"issue":"6","key":"3016_CR13","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1111\/j.1440-1843.2006.00930.x","volume":"11","author":"K Fujimoto","year":"2006","unstructured":"Fujimoto K, Kitaguchi Y, Kubo K (2006) Honda T. Clinical analysis of chronic obstructive pulmonary disease phenotypes classified using high-resolution computed tomography. Respirology 11(6):731\u2013740","journal-title":"Respirology"},{"key":"3016_CR14","doi-asserted-by":"crossref","unstructured":"Cho JL, Villacreses R, Nagpal P, Guo J, Pezzulo AA, Thurman AL, Hamzeh NY, Blount RJ, Fortis S, Hoffman EA, et al (2022) Quantitative chest CT assessment of small airways disease in post-acute sars-cov-2 infection. Radiology 304(1):185\u2013192","DOI":"10.1148\/radiol.212170"},{"issue":"10","key":"3016_CR15","doi-asserted-by":"publisher","first-page":"1791","DOI":"10.3390\/diagnostics11101791","volume":"11","author":"F Wu","year":"2021","unstructured":"Wu F, Chen L, Huang J, Fan W, Yang J, Zhang X, Jin Y, Yang F (2021) Zheng C. Total lung and lobar quantitative assessment based on paired inspiratory-expiratory chest CT in healthy adults: correlation with pulmonary ventilatory function. Diagnostics 11(10):1791","journal-title":"Diagnostics"},{"issue":"11","key":"3016_CR16","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1038\/nm.2971","volume":"18","author":"CJ Galb\u00e1n","year":"2012","unstructured":"Galb\u00e1n CJ, Han MK, Boes JL, Chughtai KA, Meyer CR, Johnson TD, Galb\u00e1n S, Rehemtulla A, Kazerooni EA, Martinez FJ et al (2012) Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat Med 18(11):1711\u20131715","journal-title":"Nat Med"},{"key":"3016_CR17","doi-asserted-by":"publisher","first-page":"2502","DOI":"10.1007\/s00330-019-06577-y","volume":"30","author":"P Konietzke","year":"2020","unstructured":"Konietzke P, Wielp\u00fctz MO, Wagner WL, Wuennemann F, Kauczor H-U, Heussel CP, Eichinger M, Eberhardt R, Gompelmann D (2020) Weinheimer O. Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval. Eur Radiol 30:2502\u20132512","journal-title":"Eur Radiol"},{"issue":"1","key":"3016_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-79336-5","volume":"11","author":"TT Ho","year":"2021","unstructured":"Ho TT, Kim T, Kim WJ, Lee CH, Chae KJ, Bak SH, Kwon SO, Jin GY, Park E-K (2021) Choi S. A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects. Sci Rep 11(1):1\u201312","journal-title":"Sci Rep"},{"key":"3016_CR19","doi-asserted-by":"crossref","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, Van Stiphout R.G, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441\u2013446","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"3016_CR20","doi-asserted-by":"crossref","unstructured":"Yun J, Cho YH, Lee SM, Hwang J, Lee JS, Oh Y-M, Lee S-D, Loh L-C, Ong C-K, Seo JB, et al (2021) Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease. Sci Rep 11(1):15144","DOI":"10.1038\/s41598-021-94535-4"},{"key":"3016_CR21","doi-asserted-by":"crossref","unstructured":"Cho YH, Seo JB, Lee SM, Kim N, Yun J, Hwang JE, Lee JS, Oh Y-M, Do\u00a0Lee S, Loh L-C, et al (2021) Radiomics approach for survival prediction in chronic obstructive pulmonary disease. Eur Radiol 31:7316\u20137324","DOI":"10.1007\/s00330-021-07747-7"},{"key":"3016_CR22","doi-asserted-by":"crossref","unstructured":"Liang C, Xu J, Wang F, Chen H, Tang J, Chen D, Li Q, Jian W, Tang G, Zheng J, et al (2021) Development of a radiomics model for predicting COPD exacerbations based on complementary visual information. In: TP41. TP041 diagnosis and risk assessment in COPD, pp 2296\u20132296. American Thoracic Society","DOI":"10.1164\/ajrccm-conference.2021.203.1_MeetingAbstracts.A2296"},{"key":"3016_CR23","doi-asserted-by":"crossref","unstructured":"Yang Y, Li W, Guo Y, Liu Y, Li Q, Yang K, Wang S, Zeng N, Duan W, Chen Z, et al (2022) Early COPD risk decision for adults aged from 40 to 79 years based on lung radiomics features. Frontiers in Medicine 9","DOI":"10.3389\/fmed.2022.845286"},{"key":"3016_CR24","doi-asserted-by":"crossref","unstructured":"Yang Y, Li W, Guo Y, Zeng N, Wang S, Chen Z, Liu Y, Chen H, Duan W, Li X, et al (2022) Lung radiomics features for characterizing and classifying COPD stage based on feature combination strategy and multi-layer perceptron classifier. Math Biosci Eng 19(8):7826\u20137855","DOI":"10.3934\/mbe.2022366"},{"issue":"5","key":"3016_CR25","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.acra.2022.01.004","volume":"29","author":"Z Li","year":"2022","unstructured":"Li Z, Liu L, Zhang Z, Yang X, Li X, Gao Y (2022) Huang K. A novel CT-based radiomics features analysis for identification and severity staging of COPD. Acad Radiol 29(5):663\u2013673","journal-title":"Acad Radiol"},{"key":"3016_CR26","unstructured":"Chen S, Ma K, Zheng Y (1904) Transfer learning for 3D medical image analysis. arXiv preprint arXiv"},{"key":"3016_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","volume":"42","author":"SM Anwar","year":"2018","unstructured":"Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42:1\u201313","journal-title":"J Med Syst"},{"key":"3016_CR28","doi-asserted-by":"publisher","first-page":"101693","DOI":"10.1016\/j.media.2020.101693","volume":"63","author":"N Tajbakhsh","year":"2020","unstructured":"Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z (2020) Ding X. Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med Image Anal 63:101693","journal-title":"Med Image Anal"},{"key":"3016_CR29","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C (2020) Sun M. Graph neural networks: a review of methods and applications. AI open 1:57\u201381","journal-title":"AI open"},{"key":"3016_CR30","doi-asserted-by":"crossref","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324","DOI":"10.1109\/TNNLS.2020.2978386"},{"issue":"8","key":"3016_CR31","doi-asserted-by":"publisher","first-page":"3141","DOI":"10.1109\/JBHI.2021.3053568","volume":"25","author":"X Song","year":"2021","unstructured":"Song X, Mao M (2021) Qian X. Auto-metric graph neural network based on a meta-learning strategy for the diagnosis of Alzheimer\u2019s disease. IEEE J Biomed Health Inform 25(8):3141\u20133152","journal-title":"IEEE J Biomed Health Inform"},{"key":"3016_CR32","doi-asserted-by":"crossref","unstructured":"McCombe N, Bamrah J, Sanchez-Bornot JM, Finn DP, McClean PL, Wong-Lin K, (ADNI) ADNI (2022) Alzheimer\u2019s disease classification using cluster-based labelling for graph neural network on heterogeneous data. Healthc Technol Lett 9(6):102\u2013109","DOI":"10.1049\/htl2.12037"},{"issue":"1","key":"3016_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-020-00173-2","volume":"4","author":"J Hofmanninger","year":"2020","unstructured":"Hofmanninger J, Prayer F, Pan J, R\u00f6hrich S, Prosch H (2020) Langs G. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp 4(1):1\u201313","journal-title":"Eur Radiol Exp"},{"key":"3016_CR34","doi-asserted-by":"crossref","unstructured":"Van\u00a0Griethuysen JJ, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RG, Fillion-Robin J-C, Pieper S, Aerts HJ. Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):104\u2013107 (2017)","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"3016_CR35","doi-asserted-by":"publisher","first-page":"4145","DOI":"10.3934\/mbe.2022191","volume":"19","author":"Y Yang","year":"2022","unstructured":"Yang Y, Li W, Kang Y, Guo Y, Yang K, Li Q, Liu Y, Yang C, Chen R, Chen H et al (2022) A novel lung radiomics feature for characterizing resting heart rate and COPD stage evolution based on radiomics feature combination strategy. Math Biosci Eng 19:4145\u20134165","journal-title":"Math Biosci Eng"},{"key":"3016_CR36","doi-asserted-by":"crossref","unstructured":"Yang Y, Chen Z, Li W, Zeng N, Guo Y, Wang S, Duan W, Liu Y, Chen H, Li X, et al (2022) Multi-modal data combination strategy based on chest HRCT images and PFT parameters for intelligent dyspnea identification in COPD. Frontiers in Medicine 9","DOI":"10.3389\/fmed.2022.980950"},{"key":"3016_CR37","doi-asserted-by":"crossref","unstructured":"Qi Y (2012) Random forest for bioinformatics. In: Ensemble machine learning: methods and applications, pp 307\u2013323. Springer","DOI":"10.1007\/978-1-4419-9326-7_11"},{"key":"3016_CR38","unstructured":"Jakkula V (2006) Tutorial on support vector machine (SVM). School of EECS, Washington State University 37(2.5):3"},{"key":"3016_CR39","doi-asserted-by":"publisher","first-page":"36825","DOI":"10.1109\/ACCESS.2018.2851382","volume":"6","author":"S Wan","year":"2018","unstructured":"Wan S, Liang Y, Zhang Y, Guizani M (2018) Deep multi-layer perceptron classifier for behavior analysis to estimate Parkinson\u2019s disease severity using smartphones. IEEE Access 6:36825\u201336833","journal-title":"IEEE Access"},{"issue":"18","key":"3016_CR40","doi-asserted-by":"publisher","first-page":"2395","DOI":"10.1161\/CIRCULATIONAHA.106.682658","volume":"117","author":"MP LaValley","year":"2008","unstructured":"LaValley MP (2008) Logistic regression. Circulation 117(18):2395\u20132399","journal-title":"Circulation"},{"key":"3016_CR41","doi-asserted-by":"crossref","unstructured":"Ayyadevara VK (2018) Gradient boosting machine. Pro machine learning algorithms: a hands-on approach to implementing algorithms in python and R, pp\u00a0117\u2013134","DOI":"10.1007\/978-1-4842-3564-5_6"},{"issue":"1998","key":"3016_CR42","first-page":"1","volume":"18","author":"S Balakrishnama","year":"1998","unstructured":"Balakrishnama S (1998) Ganapathiraju A. Linear discriminant analysis-a brief tutorial. Inst Signal Inf Process 18(1998):1\u20138","journal-title":"Inst Signal Inf Process"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03016-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03016-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03016-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T23:04:23Z","timestamp":1715123063000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03016-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["3016"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03016-z","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,16]]},"assertion":[{"value":"3 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}