{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T01:29:49Z","timestamp":1793410189849,"version":"4.5.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T00:00:00Z","timestamp":1627257600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T00:00:00Z","timestamp":1627257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (&gt;\u2009440\u00a0m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left\/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642\u20130.8373) and 0.7156 (95% CI, 0.7024\u20130.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.<\/jats:p>","DOI":"10.1038\/s41598-021-94535-4","type":"journal-article","created":{"date-parts":[[2021,7,26]],"date-time":"2021-07-26T10:11:30Z","timestamp":1627294290000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease"],"prefix":"10.1038","volume":"11","author":[{"given":"Jihye","family":"Yun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Hoon","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang Min","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeongeun","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jae Seung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeon-Mok","family":"Oh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Do","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Cher","family":"Loh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Choo-Khoon","family":"Ong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon Beom","family":"Seo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,26]]},"reference":[{"key":"94535_CR1","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1164\/rccm.202009-3533SO","volume":"203","author":"DMG Halpin","year":"2021","unstructured":"Halpin, D. M. G. et al. Global initiative for the diagnosis, management, and prevention of chronic obstructive lung disease. Am. J. Respir. Crit. Care Med. 203, 24\u201336 (2021).","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"94535_CR2","doi-asserted-by":"publisher","first-page":"W460","DOI":"10.2214\/AJR.12.10102","volume":"201","author":"JD Schroeder","year":"2013","unstructured":"Schroeder, J. D. et al. Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping, and airways in subjects with and without chronic obstructive pulmonary disease. Am. J. Roentgenol. 201, W460\u2013W470 (2013).","journal-title":"Am. J. Roentgenol."},{"key":"94535_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3348\/jksr.2018.78.1.1","volume":"78","author":"YH Cho","year":"2018","unstructured":"Cho, Y. H. et al. Quantitative CT imaging in chronic obstructive pulmonary disease: Review of current status and future challenges. J. Korean Soc. Radiol. 78, 1\u201312 (2018).","journal-title":"J. Korean Soc. Radiol."},{"key":"94535_CR4","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1007\/s00408-008-9071-0","volume":"186","author":"YK Lee","year":"2008","unstructured":"Lee, Y. K. et al. Quantitative assessment of emphysema, air trapping, and airway thickening on computed tomography. Lung 186, 157\u2013165 (2008).","journal-title":"Lung"},{"key":"94535_CR5","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/j.rmed.2010.02.023","volume":"104","author":"AA Diaz","year":"2010","unstructured":"Diaz, A. A. et al. Relationship of emphysema and airway disease assessed by CT to exercise capacity in COPD. Respir. Med. 104, 1145\u20131151 (2010).","journal-title":"Respir. Med."},{"key":"94535_CR6","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1378\/chest.09-2836","volume":"138","author":"A Haruna","year":"2010","unstructured":"Haruna, A. et al. CT scan findings of emphysema predict mortality in COPD. Chest 138, 635\u2013640 (2010).","journal-title":"Chest"},{"key":"94535_CR7","doi-asserted-by":"publisher","first-page":"1686","DOI":"10.1007\/s00330-009-1320-y","volume":"19","author":"E Cavigli","year":"2009","unstructured":"Cavigli, E. et al. Whole-lung densitometry versus visual assessment of emphysema. Eur. Radiol. 19, 1686\u20131692 (2009).","journal-title":"Eur. Radiol."},{"key":"94535_CR8","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin, P. et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441\u2013446 (2012).","journal-title":"Eur. J. Cancer"},{"key":"94535_CR9","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJ Van Griethuysen","year":"2017","unstructured":"Van Griethuysen, J. J. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104\u2013e107 (2017).","journal-title":"Cancer Res."},{"key":"94535_CR10","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1002\/jmri.25606","volume":"46","author":"S Bickelhaupt","year":"2017","unstructured":"Bickelhaupt, S. et al. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. J. Magn. Reson. Imaging 46, 604\u2013616 (2017).","journal-title":"J. Magn. Reson. Imaging"},{"key":"94535_CR11","doi-asserted-by":"publisher","first-page":"2470","DOI":"10.1007\/s00261-017-1144-1","volume":"42","author":"H Yu","year":"2017","unstructured":"Yu, H. et al. Texture analysis as a radiomic marker for differentiating renal tumors. Abdom. Radiol. 42, 2470\u20132478 (2017).","journal-title":"Abdom. Radiol."},{"key":"94535_CR12","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).","journal-title":"Nat. Commun."},{"key":"94535_CR13","doi-asserted-by":"publisher","first-page":"4003","DOI":"10.1038\/s41598-018-22357-y","volume":"8","author":"A Oikonomou","year":"2018","unstructured":"Oikonomou, A. et al. Radiomics analysis at PET\/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci. Rep. 8, 4003 (2018).","journal-title":"Sci. Rep."},{"key":"94535_CR14","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TBME.2015.2477688","volume":"63","author":"N Emaminejad","year":"2015","unstructured":"Emaminejad, N. et al. Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans. Biomed. Eng. 63, 1034\u20131043 (2015).","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"94535_CR15","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1007\/s00261-017-1072-0","volume":"42","author":"Y Zhou","year":"2017","unstructured":"Zhou, Y. et al. CT-based radiomics signature: A potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma. Abdom. Radiol. 42, 1695\u20131704 (2017).","journal-title":"Abdom. Radiol."},{"key":"94535_CR16","doi-asserted-by":"publisher","first-page":"1711","DOI":"10.1038\/nm.2971","volume":"18","author":"CJ Galb\u00e1n","year":"2012","unstructured":"Galb\u00e1n, C. J. et al. Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat. Med. 18, 1711 (2012).","journal-title":"Nat. Med."},{"key":"94535_CR17","doi-asserted-by":"publisher","first-page":"683","DOI":"10.3348\/kjr.2018.0391","volume":"20","author":"HJ Koo","year":"2019","unstructured":"Koo, H. J. et al. Prediction of pulmonary function in patients with chronic obstructive pulmonary disease: Correlation with quantitative CT parameters. Korean J. Radiol. 20, 683\u2013692 (2019).","journal-title":"Korean J. Radiol."},{"key":"94535_CR18","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.ejrad.2018.09.013","volume":"108","author":"YH Cho","year":"2018","unstructured":"Cho, Y. H. et al. Quantitative assessment of pulmonary vascular alterations in chronic obstructive lung disease: Associations with pulmonary function test and survival in the KOLD cohort. Eur. J. Radiol. 108, 276\u2013282 (2018).","journal-title":"Eur. J. Radiol."},{"key":"94535_CR19","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. Advances in Neural Information Processing Systems, 1097\u20131105."},{"key":"94535_CR20","unstructured":"Sermanet, P. et al. Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint  arXiv:1312.6229 (2013)."},{"key":"94535_CR21","unstructured":"Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint  arXiv:1409.1556 (2014)."},{"key":"94535_CR22","first-page":"1","volume-title":"Going deeper with convolutions","author":"C Szegedy","year":"2015","unstructured":"Szegedy, C. et al. Going deeper with convolutions. IEEE Conf. Comput. Vis. Pattern Recognit.1\u20139, 2015."},{"key":"94535_CR23","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1164\/rccm.201705-0860OC","volume":"197","author":"G Gonz\u00e1lez","year":"2018","unstructured":"Gonz\u00e1lez, G. et al. Disease staging and prognosis in smokers using deep learning in chest computed tomography. Am. J. Respir. Crit. Care Med. 197, 193\u2013203 (2018).","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"94535_CR24","doi-asserted-by":"publisher","first-page":"10353","DOI":"10.1038\/s41598-017-10649-8","volume":"7","author":"J Lao","year":"2017","unstructured":"Lao, J. et al. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci. Rep. 7, 10353 (2017).","journal-title":"Sci. Rep."},{"key":"94535_CR25","doi-asserted-by":"publisher","first-page":"5467","DOI":"10.1038\/s41598-017-05848-2","volume":"7","author":"Z Li","year":"2017","unstructured":"Li, Z., Wang, Y., Yu, J., Guo, Y. & Cao, W. Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci. Rep. 7, 5467 (2017).","journal-title":"Sci. Rep."},{"key":"94535_CR26","doi-asserted-by":"publisher","first-page":"169","DOI":"10.4046\/trd.2014.76.4.169","volume":"76","author":"TS Park","year":"2014","unstructured":"Park, T. S. et al. Study design and outcomes of Korean Obstructive Lung Disease (KOLD) cohort study. Tuberc. Respir. Dis. 76, 169\u2013174 (2014).","journal-title":"Tuberc. Respir. Dis."},{"key":"94535_CR27","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1093\/qjmed\/hcv109","volume":"108","author":"L-C Loh","year":"2015","unstructured":"Loh, L.-C., Oh, Y.-M. & Lee, S.-D. The Asian network for obstructive lung disease (ANOLD)-COPD from an Asian perspective. QJM 108, 921\u2013922 (2015).","journal-title":"QJM"},{"key":"94535_CR28","doi-asserted-by":"publisher","first-page":"2543","DOI":"10.2147\/COPD.S165898","volume":"13","author":"L-C Loh","year":"2018","unstructured":"Loh, L.-C. et al. A novel CT-emphysema index\/FEV1 approach of phenotyping COPD to predict mortality. Int. J. Chron. Obstruct. Pulmon. Dis. 13, 2543 (2018).","journal-title":"Int. J. Chron. Obstruct. Pulmon. Dis."},{"key":"94535_CR29","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.3346\/jkms.2011.26.12.1606","volume":"26","author":"J-W Yoo","year":"2011","unstructured":"Yoo, J.-W. et al. Comparison of clinico-physiologic and CT imaging risk factors for COPD exacerbation. J. Korean Med. Sci. 26, 1606\u20131612 (2011).","journal-title":"J. Korean Med. Sci."},{"key":"94535_CR30","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1093\/eurheartj\/ehv317","volume":"37","author":"N Gali\u00e8","year":"2015","unstructured":"Gali\u00e8, N. et al. 2015 ESC\/ERS guidelines for the diagnosis and treatment of pulmonary hypertension: The joint task force for the diagnosis and treatment of pulmonary hypertension of the European Society of Cardiology (ESC) and the European Respiratory Society (ERS): Endorsed by: Association for European Paediatric and Congenital Cardiology (AEPC), International Society for Heart and Lung Transplantation (ISHLT). Eur. Heart J. 37, 67\u2013119 (2015).","journal-title":"Eur. Heart J."},{"key":"94535_CR31","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1214\/08-AOAS169","volume":"2","author":"H Ishwaran","year":"2008","unstructured":"Ishwaran, H., Kogalur, U. B., Blackstone, E. H. & Lauer, M. S. Random survival forests. Ann. Appl. Stat. 2, 841\u2013860 (2008).","journal-title":"Ann. Appl. Stat."},{"key":"94535_CR32","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1111\/j.0006-341X.2000.00337.x","volume":"56","author":"PJ Heagerty","year":"2000","unstructured":"Heagerty, P. J., Lumley, T. & Pepe, M. S. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56, 337\u2013344 (2000).","journal-title":"Biometrics"},{"key":"94535_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-017-0332-6","volume":"17","author":"AN Kamarudin","year":"2017","unstructured":"Kamarudin, A. N., Cox, T. & Kolamunnage-Dona, R. Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Med. Res. Methodol. 17, 1\u201319 (2017).","journal-title":"BMC Med. Res. Methodol."},{"key":"94535_CR34","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.ejrad.2016.08.022","volume":"86","author":"M Kirby","year":"2017","unstructured":"Kirby, M. et al. Management of COPD: Is there a role for quantitative imaging?. Eur. J. Radiol. 86, 335\u2013342 (2017).","journal-title":"Eur. J. Radiol."},{"key":"94535_CR35","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1164\/rccm.201703-0451PP","volume":"196","author":"WW Labaki","year":"2017","unstructured":"Labaki, W. W. et al. The role of chest computed tomography in the evaluation and management of the patient with chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 196, 1372\u20131379 (2017).","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"94535_CR36","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1164\/rccm.201209-1722OC","volume":"187","author":"A Johannessen","year":"2013","unstructured":"Johannessen, A. et al. Mortality by level of emphysema and airway wall thickness. Am. J. Respir. Crit. Care Med. 187, 602\u2013608 (2013).","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"94535_CR37","doi-asserted-by":"publisher","first-page":"952","DOI":"10.1016\/j.chest.2020.02.079","volume":"158","author":"M Moll","year":"2020","unstructured":"Moll, M. et al. Machine learning and prediction of all-cause mortality in COPD. Chest 158, 952\u2013964 (2020).","journal-title":"Chest"},{"key":"94535_CR38","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-021-07747-7","author":"YH Cho","year":"2021","unstructured":"Cho, Y. H. et al. Radiomics approach for survival prediction in chronic obstructive pulmonary disease. Eur. Radiol. https:\/\/doi.org\/10.1007\/s00330-021-07747-7 (2021).","journal-title":"Eur. Radiol."},{"key":"94535_CR39","unstructured":"Korea National Health and Nutrition Examination Surveys. https:\/\/kosis.kr\/ (2018)."},{"key":"94535_CR40","doi-asserted-by":"publisher","first-page":"01","DOI":"10.18332\/tid\/82190","volume":"16","author":"KH Lim","year":"2018","unstructured":"Lim, K. H. et al. Prevalence and factors associated with smoking among adults in Malaysia: Findings from the National Health and Morbidity Survey (NHMS) 2015. Tob. Induc. Dis. 16, 01 (2018).","journal-title":"Tob. Induc. Dis."},{"key":"94535_CR41","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1183\/09031936.00096108","volume":"33","author":"J De Torres","year":"2009","unstructured":"De Torres, J. et al. Sex differences in mortality in patients with COPD. Eur. Respir. J. 33, 528\u2013535 (2009).","journal-title":"Eur. Respir. J."},{"key":"94535_CR42","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1164\/rccm.201512-2379ED","volume":"193","author":"PJ Barnes","year":"2016","unstructured":"Barnes, P. J. Sex differences in chronic obstructive pulmonary disease mechanisms. Am. J. Respir. Crit. Care Med. 193, 813\u2013824 (2016).","journal-title":"Am. J. Respir. Crit. Care Med."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-94535-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-94535-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-94535-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T11:44:19Z","timestamp":1670067859000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-94535-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,26]]},"references-count":42,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["94535"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-94535-4","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,26]]},"assertion":[{"value":"17 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"15144"}}