{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T19:56:48Z","timestamp":1769630208143,"version":"3.49.0"},"reference-count":60,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871251"],"award-info":[{"award-number":["61871251"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81561168023"],"award-info":[{"award-number":["81561168023"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61601019"],"award-info":[{"award-number":["61601019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871022"],"award-info":[{"award-number":["61871022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"111 project","doi-asserted-by":"crossref","award":["B13003"],"award-info":[{"award-number":["B13003"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Natural Science Foundation","award":["7202102"],"award-info":[{"award-number":["7202102"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s10278-021-00455-0","type":"journal-article","created":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T18:03:58Z","timestamp":1620410638000},"page":"605-617","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Improving the Subtype Classification of Non-small Cell Lung Cancer by Elastic Deformation Based Machine Learning"],"prefix":"10.1007","volume":"34","author":[{"given":"Yang","family":"Gao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanglei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianwen","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,7]]},"reference":[{"issue":"24","key":"455_CR1","doi-asserted-by":"publisher","first-page":"13790","DOI":"10.1073\/pnas.191502998","volume":"98","author":"A Bhattacharjee","year":"2001","unstructured":"Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences. 2001; 98(24):13790-13795.","journal-title":"Proceedings of the National Academy of Sciences."},{"issue":"1","key":"455_CR2","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3322\/caac.20138","volume":"62","author":"R Siegel","year":"2012","unstructured":"Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62(1):10-29.","journal-title":"CA Cancer J Clin."},{"issue":"2","key":"455_CR3","doi-asserted-by":"publisher","first-page":"451","DOI":"10.4143\/crt.2016.092","volume":"48","author":"K-W Jung","year":"2016","unstructured":"Jung K-W, Won Y-J, Oh C-M, et al. Prediction of Cancer Incidence and Mortality in Korea, 2016. Cancer research and treatment : official journal of Korean Cancer Association. 2016; 48(2):451-457.","journal-title":"Cancer research and treatment : official journal of Korean Cancer Association."},{"key":"455_CR4","unstructured":"Center NC. China Cancer Report: 2017. Beijing 2017."},{"key":"455_CR5","unstructured":"Travis WD. Pathology & genetics tumours of the lung, pleura, thymus and heart. World Health Organization classification of tumours. 2004."},{"issue":"1","key":"455_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/ijc.23605","volume":"123","author":"A Risch","year":"2008","unstructured":"Risch A, Plass C. Lung cancer epigenetics and genetics. International Journal of Cancer. 2008;123(1):1-7.","journal-title":"International Journal of Cancer."},{"issue":"13","key":"455_CR7","doi-asserted-by":"publisher","first-page":"5099","DOI":"10.1073\/pnas.86.13.5099","volume":"86","author":"A Weston","year":"1989","unstructured":"Weston A, Willey JC, Modali R, et al. Differential DNA sequence deletions from chromosomes 3, 11, 13, and 17 in squamous-cell carcinoma, large-cell carcinoma, and adenocarcinoma of the human lung. Proceedings of the National Academy of Sciences. 1989; 86(13):5099-5103.","journal-title":"Proceedings of the National Academy of Sciences."},{"issue":"2","key":"455_CR8","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.lungcan.2013.07.025","volume":"82","author":"LA Pikor","year":"2013","unstructured":"Pikor LA, Ramnarine VR, Lam S, Lam WL. Genetic alterations defining NSCLC subtypes and their therapeutic implications. Lung Cancer. 2013; 82(2):179-189.","journal-title":"Lung Cancer."},{"issue":"11","key":"455_CR9","doi-asserted-by":"publisher","first-page":"2184","DOI":"10.1200\/JCO.2004.11.022","volume":"22","author":"DH Johnson","year":"2004","unstructured":"Johnson DH, Fehrenbacher L, Novotny WF, et al. Randomized Phase II Trial Comparing Bevacizumab Plus Carboplatin and Paclitaxel With Carboplatin and Paclitaxel Alone in Previously Untreated Locally Advanced or Metastatic Non-Small-Cell Lung Cancer. J Clin Oncol. 2004; 22(11):2184-2191.","journal-title":"J Clin Oncol."},{"issue":"21","key":"455_CR10","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1200\/JCO.2007.15.0375","volume":"26","author":"GV Scagliotti","year":"2008","unstructured":"Scagliotti GV, Parikh P, von Pawel J, et al. Phase III Study Comparing Cisplatin Plus Gemcitabine With Cisplatin Plus Pemetrexed in Chemotherapy-Naive Patients With Advanced-Stage Non\u2013Small-Cell Lung Cancer. J Clin Oncol. 2008; 26(21):3543-3551.","journal-title":"J Clin Oncol."},{"issue":"3","key":"455_CR11","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1634\/theoncologist.2008-0232","volume":"14","author":"G Scagliotti","year":"2009","unstructured":"Scagliotti G, Hanna N, Fossella F, et al. The Differential Efficacy of Pemetrexed According to NSCLC Histology: A Review of Two Phase III Studies. The Oncologist. 2009; 14(3):253-263.","journal-title":"The Oncologist."},{"issue":"3","key":"455_CR12","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1053\/j.ro.2011.02.003","volume":"46","author":"WD Travis","year":"2011","unstructured":"Travis WD. Classification of Lung Cancer. Semin Roentgenol. 2011; 46(3):178-186.","journal-title":"Semin Roentgenol."},{"issue":"5","key":"455_CR13","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/j.nano.2011.10.001","volume":"8","author":"O Barash","year":"2012","unstructured":"Barash O, Peled N, Tisch U, Bunn PA, Hirsch FR, Haick H. Classification of lung cancer histology by gold nanoparticle sensors. Nanomed Nanotechnol Biol Med. 2012; 8(5):580-589.","journal-title":"Nanomed Nanotechnol Biol Med."},{"issue":"6","key":"455_CR14","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1016\/j.ejca.2012.11.021","volume":"49","author":"T Cufer","year":"2013","unstructured":"Cufer T, Ovcaricek T, O\u2019Brien MER. Systemic therapy of advanced non-small cell lung cancer: Major-developments of the last 5-years. Eur J Cancer. 2013; 49(6):1216-1225.","journal-title":"Eur J Cancer."},{"issue":"10","key":"455_CR15","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1056\/NEJMoa0810699","volume":"361","author":"TS Mok","year":"2009","unstructured":"Mok TS, Wu YL, Thongprasert S, et al. Gefitinib or Carboplatin\u2013Paclitaxel in Pulmonary Adenocarcinoma. N Engl J Med. 2009; 361(10):947-957.","journal-title":"N Engl J Med."},{"issue":"19","key":"455_CR16","doi-asserted-by":"publisher","first-page":"4875","DOI":"10.1158\/0008-5472.CAN-12-2217","volume":"72","author":"C Swanton","year":"2012","unstructured":"16. Swanton C. Intratumor heterogeneity: evolution through space and time. Cancer Res. 2012; 72(19):4875-4882.","journal-title":"Cancer Res."},{"key":"455_CR17","doi-asserted-by":"crossref","unstructured":"Wu W, Parmar C, Grossmann P, et al. Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology. Front Oncol. 2016; 6(71).","DOI":"10.3389\/fonc.2016.00071"},{"key":"455_CR18","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1016\/j.compbiomed.2017.10.029","volume":"91","author":"M Saad","year":"2017","unstructured":"Saad M, Choi TS. Deciphering unclassified tumors of non-small-cell lung cancer through radiomics. Comput Biol Med. 2017; 91:222-230.","journal-title":"Comput Biol Med."},{"key":"455_CR19","doi-asserted-by":"crossref","unstructured":"E L, Lu L, Li L, Yang H, Schwartz LH, Zhao B. Radiomics for Classification of Lung Cancer Histological Subtypes Based on Nonenhanced Computed Tomography. Acad Radiol. 2018.","DOI":"10.1016\/j.acra.2018.10.013"},{"key":"455_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.04.003","volume":"67","author":"M Saad","year":"2018","unstructured":"Saad M, Choi TS. Computer-assisted subtyping and prognosis for non-small cell lung cancer patients with unresectable tumor. Comput Med Imaging Graph. 2018; 67:1-8.","journal-title":"Comput Med Imaging Graph."},{"key":"455_CR21","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nature Reviews Clinical Oncology. 2017; 14:749.","journal-title":"Nature Reviews Clinical Oncology."},{"issue":"3","key":"455_CR22","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.radonc.2018.03.033","volume":"127","author":"S Sanduleanu","year":"2018","unstructured":"Sanduleanu S, Woodruff HC, de Jong EEC, et al. Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score. Radiother Oncol. 2018; 127(3):349-360.","journal-title":"Radiother Oncol."},{"key":"455_CR23","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.lungcan.2017.10.015","volume":"115","author":"R Thawani","year":"2018","unstructured":"Thawani R, McLane M, Beig N, et al. Radiomics and radiogenomics in lung cancer: A review for the clinician. Lung Cancer. 2018; 115:34-41.","journal-title":"Lung Cancer."},{"issue":"1","key":"455_CR24","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s12194-017-0433-2","volume":"11","author":"A Haga","year":"2018","unstructured":"Haga A, Takahashi W, Aoki S, et al. Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis. Radiological Physics and Technology. 2018; 11(1):27-35.","journal-title":"Radiological Physics and Technology."},{"key":"455_CR25","volume-title":"Chest x-ray generation and data augmentation for cardiovascular abnormality classification","author":"A Madani","year":"2018","unstructured":"Madani A, Moradi M, Karargyris A, Syeda-Mahmood T. Chest x-ray generation and data augmentation for cardiovascular abnormality classification. Paper presented at: SPIE Medical Imaging2018."},{"issue":"7","key":"455_CR26","doi-asserted-by":"publisher","first-page":"2772","DOI":"10.1007\/s00330-017-5221-1","volume":"28","author":"X Zhu","year":"2018","unstructured":"Zhu X, Dong D, Chen Z, et al. Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol. 2018; 28(7):2772-2778.","journal-title":"Eur Radiol."},{"issue":"398","key":"455_CR27","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1080\/01621459.1987.10478458","volume":"82","author":"MA Tanner","year":"1987","unstructured":"Tanner MA, Wong WH. The Calculation of Posterior Distributions by Data Augmentation. J Am Stat Assoc. 1987; 82(398):528-540.","journal-title":"J Am Stat Assoc."},{"issue":"1","key":"455_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1198\/10618600152418584","volume":"10","author":"DA van Dyk","year":"2001","unstructured":"van Dyk DA, Meng X-L. The Art of Data Augmentation. Journal of Computational and Graphical Statistics. 2001; 10(1):1-50.","journal-title":"Journal of Computational and Graphical Statistics."},{"key":"455_CR29","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. Paper presented at: Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015; 2015\/\/, 2015; Cham.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"455_CR30","unstructured":"Simard PY, Steinkraus D, Platt JC. Best practices for convolutional neural networks applied to visual document analysis. Paper presented at: Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.; 6\u20136 Aug. 2003, 2003."},{"key":"455_CR31","doi-asserted-by":"crossref","unstructured":"Dosovitskiy A, Springenberg JT, Riedmiller M, Brox T. Discriminative Unsupervised Feature Learning with Convolutional Neural Networks. 2014:766--774.","DOI":"10.1109\/CVPR.2015.7298761"},{"key":"455_CR32","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.cmpb.2018.01.017","volume":"157","author":"MA Al-masni","year":"2018","unstructured":"Al-masni MA, Al-antari MA, Park J-M, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed. 2018; 157(0):85-94.","journal-title":"Comput Methods Programs Biomed."},{"issue":"7","key":"455_CR33","doi-asserted-by":"publisher","first-page":"3244","DOI":"10.1364\/BOE.9.003244","volume":"9","author":"SK Devalla","year":"2018","unstructured":"Devalla SK, Renukanand PK, Sreedhar BK, et al. DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images. Biomedical optics express. 2018; 9(7):3244-3265.","journal-title":"Biomedical optics express."},{"key":"455_CR34","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.compbiomed.2017.05.010","volume":"86","author":"J Ramos-Gonz\u00e1lez","year":"2017","unstructured":"Ramos-Gonz\u00e1lez J, L\u00f3pez-S\u00e1nchez D, Castellanos-Garz\u00f3n JA, de Paz JF, Corchado JM. A CBR framework with gradient boosting based feature selection for lung cancer subtype classification. Comput Biol Med. 2017; 86:98-106.","journal-title":"Comput Biol Med."},{"issue":"4","key":"455_CR35","doi-asserted-by":"publisher","first-page":"e12901","DOI":"10.1111\/eci.12901","volume":"48","author":"M Rabbani","year":"2018","unstructured":"Rabbani M, Kanevsky J, Kafi K, Chandelier F, Giles FJ. Role of artificial intelligence in the care of patients with nonsmall cell lung cancer. Eur J Clin Invest. 2018; 48(4):e12901.","journal-title":"Eur J Clin Invest."},{"key":"455_CR36","doi-asserted-by":"crossref","unstructured":"Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning. Nat Med. 2018.","DOI":"10.1101\/197574"},{"issue":"17","key":"455_CR37","first-page":"4671","volume":"54","author":"H Pedersen","year":"1994","unstructured":"Pedersen H, Br\u00fcnner N, Francis D, et al. Prognostic Impact of Urokinase, Urokinase Receptor, and Type 1 Plasminogen Activator Inhibitor in Squamous and Large Cell Lung Cancer Tissue. Cancer Res. 1994; 54(17):4671-4675.","journal-title":"Cancer Res."},{"issue":"8","key":"455_CR38","doi-asserted-by":"publisher","first-page":"S851","DOI":"10.1097\/01.JTO.0000284677.33344.62","volume":"2","author":"P Peterson","year":"2007","unstructured":"Peterson P, Park K, Fossella F, Gatzemeier U, John W, Scagliotti G. P2-328: Is pemetrexed more effective in adenocarcinoma and large cell lung cancer than in squamous cell carcinoma? A retrospective analysis of a phase III trial of pemetrexed vs docetaxel in previously treated patients with advanced non-small cell lung cancer (NSCLC). J Thorac Oncol. 2007; 2(8):S851.","journal-title":"J Thorac Oncol."},{"key":"455_CR39","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1038\/modpathol.2009.30","volume":"22","author":"V Monica","year":"2009","unstructured":"Monica V, Ceppi P, Righi L, et al. Desmocollin-3: a new marker of squamous differentiation in undifferentiated large-cell carcinoma of the lung. Modern Pathol. 2009; 22:709.","journal-title":"Modern Pathol."},{"issue":"3","key":"455_CR40","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.1007\/s13277-014-2773-4","volume":"36","author":"G-Y Zhao","year":"2015","unstructured":"Zhao G-Y, Lin Z-W, Lu C-L, et al. USP7 overexpression predicts a poor prognosis in lung squamous cell carcinoma and large cell carcinoma. Tumor Biol. 2015; 36(3):1721-1729.","journal-title":"Tumor Biol."},{"issue":"3","key":"455_CR41","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1039\/C4MB00659C","volume":"11","author":"Z Cai","year":"2015","unstructured":"Cai Z, Xu D, Zhang Q, Zhang J, Ngai S-M, Shao J. Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol Biosyst. 2015; 11(3):791-800.","journal-title":"Mol Biosyst."},{"issue":"6","key":"455_CR42","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. J Digit Imaging. 2013; 26(6):1045-1057.","journal-title":"J Digit Imaging."},{"key":"455_CR43","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJWL Aerts","year":"2014","unstructured":"Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature communications. 2014; 5(0):4006.","journal-title":"Nature communications."},{"key":"455_CR44","unstructured":"Aerts HJWL, Rios Velazquez E, Leijenaar RTH, et al. Data From NSCLC-Radiomics. In: Archive TCI, ed2015."},{"key":"455_CR45","doi-asserted-by":"crossref","unstructured":"Maayan Frid-Adar ID, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan. GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification. ArXiv. 2018; 1803(01229).","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"455_CR46","doi-asserted-by":"crossref","unstructured":"Beig N, Khorrami M, Alilou M, et al. Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology. 2018:180910.","DOI":"10.1148\/radiol.2018180910"},{"key":"455_CR47","unstructured":"Bradski G. The OpenCV Library. Dr Dobb's Journal of Software Tools. 2000:2236121."},{"key":"455_CR48","doi-asserted-by":"crossref","unstructured":"Saeb S, Lonini L, Jayaraman A, Mohr DC, Kording KP. Voodoo Machine Learning for Clinical Predictions. bioRxiv. 2016.","DOI":"10.1101\/059774"},{"key":"455_CR49","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011; 12(0):2825-2830.","journal-title":"Journal of Machine Learning Research."},{"key":"455_CR50","unstructured":"Muller A, Guido S. Introduction to machine learning with python. O'Reilly; 2016."},{"issue":"1","key":"455_CR51","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1186\/s12931-018-0887-8","volume":"19","author":"B He","year":"2018","unstructured":"He B, Zhao W, Pi J-Y, et al. A biomarker basing on radiomics for the prediction of overall survival in non\u2013small cell lung cancer patients. Respir Res. 2018; 19(1):199.","journal-title":"Respir Res."},{"key":"455_CR52","unstructured":"W.D. Travis EB, A.P. Burke, A. Marx, A.G. Nicholson (Eds.). WHO classification of tumours of the lung, pleura, thymus and heart (4th ed.). International Agency for Research on Cancer, Lyon, France; 2015."},{"issue":"4","key":"455_CR53","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1109\/TMI.2016.2640180","volume":"36","author":"A Pezeshk","year":"2017","unstructured":"Pezeshk A, Petrick N, Chen W, Sahiner B. Seamless Lesion Insertion for Data Augmentation in CAD Training. IEEE Trans Med Imaging. 2017; 36(4):1005-1015.","journal-title":"IEEE Trans Med Imaging."},{"issue":"2","key":"455_CR54","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1148\/radiol.12111607","volume":"264","author":"O Gevaert","year":"2012","unstructured":"Gevaert O, Xu J, Hoang CD, et al. Non\u2013Small Cell Lung Cancer: Identifying Prognostic Imaging Biomarkers by Leveraging Public Gene Expression Microarray Data\u2014Methods and Preliminary Results. Radiology. 2012; 264(2):387-396.","journal-title":"Radiology."},{"key":"455_CR55","unstructured":"Bakr S, Gevaert O, Echegaray S, et al. Data for NSCLC Radiogenomics Collection. In: Archive TCI, ed2017."},{"key":"455_CR56","doi-asserted-by":"publisher","first-page":"180202","DOI":"10.1038\/sdata.2018.202","volume":"5","author":"S Bakr","year":"2018","unstructured":"Bakr S, Gevaert O, Echegaray S, et al. A radiogenomic dataset of non-small cell lung cancer. Scientific Data. 2018; 5:180202.","journal-title":"Scientific Data."},{"issue":"10","key":"455_CR57","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1097\/JTO.0000000000000286","volume":"9","author":"OCJ Schuurbiers","year":"2014","unstructured":"Schuurbiers OCJ, Meijer TWH, Kaanders JHAM, et al. Glucose Metabolism in NSCLC Is Histology-Specific and Diverges the Prognostic Potential of 18FDG-PET for Adenocarcinoma and Squamous Cell Carcinoma. J Thorac Oncol. 2014; 9(10):1485-1493.","journal-title":"J Thorac Oncol."},{"issue":"7","key":"455_CR58","doi-asserted-by":"publisher","first-page":"3091","DOI":"10.1002\/mp.13551","volume":"46","author":"J Liu","year":"2019","unstructured":"Liu J, Cui J, Liu F, Yuan Y, Guo F, Zhang G. Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model. Med Phys. 2019; 46(7):3091-3100.","journal-title":"Med Phys."},{"key":"455_CR59","doi-asserted-by":"crossref","unstructured":"Neto ACdS, Diniz PHB, Diniz JOB, et al. Diagnosis of Non-Small Cell Lung Cancer Using Phylogenetic Diversity in Radiomics Context. Image Analysis and Recognition. 2018:598\u2013604.","DOI":"10.1007\/978-3-319-93000-8_68"},{"key":"455_CR60","doi-asserted-by":"crossref","unstructured":"Han Y, Ma Y, Wu Z, et al. Histologic subtype classification of non-small cell lung cancer using PET\/CT images. Eur J Nucl Med Mol Imaging. 2020.","DOI":"10.1007\/s00259-020-04771-5"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00455-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-021-00455-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-021-00455-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T17:12:00Z","timestamp":1627924320000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-021-00455-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,7]]},"references-count":60,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["455"],"URL":"https:\/\/doi.org\/10.1007\/s10278-021-00455-0","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,7]]},"assertion":[{"value":"14 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}