{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T06:09:29Z","timestamp":1790834969101,"version":"4.1.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:00:00Z","timestamp":1657152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T00:00:00Z","timestamp":1657152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100013779","name":"Sabzevar University of Medical Sciences","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100013779","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Phys Eng Sci Med"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s13246-022-01140-4","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T08:02:49Z","timestamp":1657180969000},"page":"747-755","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Detection of stage of lung changes in COVID-19 disease based on CT images: a radiomics approach"],"prefix":"10.1007","volume":"45","author":[{"given":"Mohammad","family":"Mehrpouyan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Zamanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghazal","family":"Mehri-Kakavand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamad","family":"Pursamimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1595-7281","authenticated-orcid":false,"given":"Ahmad","family":"Shalbaf","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Ghorbani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amirhossein","family":"Abbaskhani Davanloo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"1140_CR1","first-page":"100","volume":"100","author":"C Sohrabi","year":"2020","unstructured":"Sohrabi C, Alsafi Z, O\u2019Neill N, Khan M, Kerwan A, Al-Jabir A et al (2020) World Health Organization declares global emergency: a review of the 2019 novel coronavirus (COVID-19). Int J Surg 76:71\u201376","journal-title":"International Journal of Surgery"},{"key":"1140_CR2","first-page":"115","volume":"100","author":"L Pan","year":"2020","unstructured":"Pan L, Mu M, Yang P, Sun Y, Wang R, Yan J et al (2020) Clinical characteristics of COVID-19 patients with digestive symptoms in Hubei, China: a descriptive, cross-sectional, multicenter study. Am J Gastroenterol 115(5):766\u2013773","journal-title":"The American journal of gastroenterology"},{"issue":"2","key":"1140_CR3","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1016\/j.radi.2020.09.010","volume":"27","author":"W Alsharif","year":"2021","unstructured":"Alsharif W, Qurashi A (2021) Effectiveness of COVID-19 diagnosis and management tools: a review. Radiography 27(2):682\u2013687","journal-title":"Radiography"},{"issue":"2","key":"1140_CR4","doi-asserted-by":"publisher","first-page":"E115","DOI":"10.1148\/radiol.2020200432","volume":"296","author":"Y Fang","year":"2020","unstructured":"Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P et al (2020) Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296(2):E115\u2013E7","journal-title":"Radiology"},{"issue":"3","key":"1140_CR5","doi-asserted-by":"publisher","first-page":"200463","DOI":"10.1148\/radiol.2020200463","volume":"295","author":"A Bernheim","year":"2020","unstructured":"Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Ning Zhang N (2020) Chest CT findings in coronavirus disease-19 (covid-19): relationship to duration of infection. Radiology 295(3):200463","journal-title":"Radiology"},{"issue":"1","key":"1140_CR6","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.jcct.2020.08.013","volume":"15","author":"G Pontone","year":"2021","unstructured":"Pontone G, Scafuri S, Mancini ME, Agalbato C, Guglielmo M, Baggiano A et al (2021) Role of computed tomography in COVID-19. J Cardiovasc Comput Tomogr 15(1):27\u201336","journal-title":"J Cardiovasc Comput Tomogr"},{"issue":"1","key":"1140_CR7","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1148\/radiol.2017161659","volume":"284","author":"H MacMahon","year":"2017","unstructured":"MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR et al (2017) Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 284(1):228\u2013243","journal-title":"Radiology"},{"issue":"3","key":"1140_CR8","first-page":"719","volume":"38","author":"HJ Koo","year":"2018","unstructured":"Koo HJ, Lim S, Choe J, Choi SH, Sung H, Do KH (2018) Radiographic and CT features of viral pneumonia. Radio Graphics 38(3):719\u2013739","journal-title":"Radio Graphics"},{"issue":"3","key":"1140_CR9","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1148\/radiol.2020200370","volume":"295","author":"F Pan","year":"2020","unstructured":"Pan F, Ye T, Sun P, Gui S, Liang B, Li L et al (2020) Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology 295(3):715\u2013721","journal-title":"Radiology"},{"issue":"5","key":"1140_CR10","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.acra.2020.03.003","volume":"27","author":"M Li","year":"2020","unstructured":"Li M, LeiP, Zeng B, Li Z, Yu P, Fan B et al (2020) Coronavirus disease (COVID-19): spectrum of CT findings and temporal progression of the disease. Acad Radiol 27(5):603\u2013608","journal-title":"Acad Radiol"},{"issue":"2","key":"1140_CR11","doi-asserted-by":"publisher","first-page":"367","DOI":"10.2214\/AJR.20.23078","volume":"215","author":"G Huang","year":"2020","unstructured":"Huang G, Gong T, Wang G, Wang J, Guo X, Cai E et al (2020) Timely diagnosis and treatment shortens the time to resolution of coronavirus disease (COVID-19) pneumonia and lowers the highest and last CT scores from sequential chest CT. Am J Roentgenol 215(2):367\u2013373","journal-title":"Am J Roentgenol"},{"issue":"1","key":"1140_CR12","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1186\/s43055-020-00223-0","volume":"51","author":"-MuayadSO Al-Tameemi","year":"2020","unstructured":"Al-Tameemi -MuayadSO, Alghazali H, Abed M, Abu Ghniem M, Hawiji MN (2020) Pulmonary CT manifestations of COVID-19: changes within 2 weeks duration from presentation. Egypt J Radiol Nucl Med 51(1):105","journal-title":"Egypt J Radiol Nucl Med"},{"key":"1140_CR13","doi-asserted-by":"publisher","first-page":"19649","DOI":"10.1038\/s41598-020-76776-x","volume":"10","author":"Z Zhang","year":"2020","unstructured":"Zhang Z, Tang R, Sun H, Dai H, Chen K, Ye X et al (2020) Temporal lung changes on thin-section CT in patients with COVID-19 pneumonia. Sci Rep 10:19649","journal-title":"Sci Rep"},{"key":"1140_CR14","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/s13246-021-01034-x","volume":"44","author":"L Holloway","year":"2021","unstructured":"Holloway L, Bezak E, Baldock C (2021) Artificial intelligence (AI) will enable improved diagnosis and treatment outcomes. Phys Eng Sci Med 44:603\u2013606","journal-title":"Phys Eng Sci Med"},{"key":"1140_CR15","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s13246-021-00977-5","volume":"44","author":"H Ketabi","year":"2021","unstructured":"Ketabi H, Ekhlasi A, Ahmadi H (2021) A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine. Phys Eng Sci Med 44:277\u2013290","journal-title":"Phys Eng Sci Med"},{"key":"1140_CR16","doi-asserted-by":"crossref","unstructured":"Sani ZA, Shalbaf A, Behnam H, Shalbaf R (2015) Automatic computation of left ventricular volume changes over a cardiac cycle from echocardiography images by nonlinear dimensionality reduction. J Digit Imaging 28(1):91\u201398","DOI":"10.1007\/s10278-014-9722-z"},{"issue":"5","key":"1140_CR17","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1007\/s11548-012-0786-2","volume":"7","author":"A Shalbaf","year":"2012","unstructured":"Shalbaf A, Behnam H, Alizade-Sani Z, Shojaifard M (2012) Left ventricle wall motion quantification from echocardiographic images by non-rigid image registration. Int J Comput Assist Radiol Surg 7(5):769\u2013783","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"1140_CR18","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/s13246-021-00993-5","volume":"44","author":"P Mirmohammadi","year":"2021","unstructured":"Mirmohammadi P, Ameri M, Shalbaf A (2021) Recognition of acute lymphoblastic leukemia and lymphocytes cell subtypes in microscopic images using random forest classifier. Phys Eng Sci Med 44:433\u2013441","journal-title":"Phys Eng Sci Med"},{"key":"1140_CR19","doi-asserted-by":"crossref","unstructured":"Sahoo P, Roy I, Ahlawat R et al (2021) Potential diagnosis of COVID-19 from chest X-ray and CT findings using semi-supervised learning.Phys Eng Sci Med 45:31\u201342","DOI":"10.1007\/s13246-021-01075-2"},{"key":"1140_CR20","doi-asserted-by":"crossref","unstructured":"Gillman AG, Lunardo F, Prinable J et al (2021) Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Phys Eng Sci Med 45:13\u201329","DOI":"10.1007\/s13246-021-01093-0"},{"key":"1140_CR21","doi-asserted-by":"crossref","unstructured":"Ouyang X, Huo J, Xia L, Shan F, Liu J, Mo Z et al (2020) Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia. IEEE Trans Med Imaging 39(8):2595\u20132605","DOI":"10.1109\/TMI.2020.2995508"},{"key":"1140_CR22","first-page":"682","volume":"9","author":"L Lin","year":"2021","unstructured":"Lin L, Liu J, Deng Q, Li N, Pan J, Sun H et al (2021) Radiomics is effective for distinguishing coronavirus disease 2019 pneumonia from influenza virus pneumonia. Front Public Health 9:682","journal-title":"Front Public Health"},{"issue":"1","key":"1140_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-79139-8","volume":"11","author":"N Yang","year":"2021","unstructured":"Yang N, Liu F, Li C, Xiao W, Xie S, Yuan S et al (2021) Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images. Sci Rep 11(1):1\u20137","journal-title":"Sci Rep"},{"key":"1140_CR24","unstructured":"Kadry S, Rajinikanth V, Rho S, Raja NSM, Rao VS, Thanaraj KP (2020) Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal\/COVID-19 Class. arXiv preprint. arXiv:2004.13122"},{"issue":"6","key":"1140_CR25","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1097\/RTI.0000000000000544","volume":"16","author":"L Fu","year":"2020","unstructured":"Fu L, Li Y, Cheng A et al (2020) A novel machine learning-derived radiomic signature of the whole lung differentiates stable from progressive COVID-19 infection: a retrospective cohort study. J Thorac Imaging 16(6):361\u2013368","journal-title":"J Thorac Imaging"},{"key":"1140_CR26","doi-asserted-by":"publisher","first-page":"8926","DOI":"10.1038\/s41598-020-65731-5","volume":"10","author":"HB Tan","year":"2020","unstructured":"Tan HB, Xiong F, Jiang YL et al (2020) The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia. Sci Rep 10:8926","journal-title":"Sci Rep"},{"key":"1140_CR27","doi-asserted-by":"crossref","unstructured":"Wang H, Wang L, Edward H et al Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.Eur J Nucl Med Mol Imaging2020:1\u20139","DOI":"10.1007\/s00259-020-05075-4"},{"issue":"1","key":"1140_CR28","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3390\/diagnostics11010041","volume":"11","author":"J Guiot","year":"2020","unstructured":"Guiot J, Vaidyanathan A, Deprez L et al (2020) development and validation of an automated radiomic CT signature for detecting COVID-19. Diagnostics (Basel) 11(1):41","journal-title":"Diagnostics (Basel)"},{"key":"1140_CR29","doi-asserted-by":"publisher","first-page":"109552","DOI":"10.1016\/j.ejrad.2021.109552","volume":"136","author":"L Wang","year":"2021","unstructured":"Wang L, Kelly B, Lee EH et al (2021) Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features. Eur J Radiol 136:109552","journal-title":"Eur J Radiol"},{"issue":"1","key":"1140_CR30","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12967-020-02692-3","volume":"19","author":"H Liu","year":"2021","unstructured":"Liu H, Ren H, Wu Z et al (2021) CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS. J Transl Med 19(1):29","journal-title":"J Transl Med"},{"issue":"2","key":"1140_CR31","first-page":"e200075","volume":"2","author":"L Huang","year":"2020","unstructured":"Huang L, Han R, Ai T, Yu P, Kang H, Tao Q et al (2020) Serial quantitative chest ct assessment of covid-19: deep-learning approach. Radiol Cardiothorac Imaging 2(2):e200075","journal-title":"Radiology: Cardiothorac Imaging"},{"key":"1140_CR32","unstructured":"Shan F, Gao Y, Wang J, Shi W, Shi N, Han M et al (2020) Lung infection quantification of COVID-19 in CT images with deep learning. arXiv preprint. arXiv:2003.04655"},{"key":"1140_CR33","doi-asserted-by":"crossref","unstructured":"Novitasari DCR, Lubab A, Sawiji A et al (2019) Application of feature extraction for breast cancer using one order statistic, GLCM, GLRLM, and GLDM. Adv Sci Technol Eng Syst J 4(4):115\u2013120","DOI":"10.25046\/aj040413"},{"issue":"14","key":"1140_CR34","doi-asserted-by":"publisher","first-page":"5471","DOI":"10.1088\/0031-9155\/60\/14\/5471","volume":"60","author":"M Valli\u00e8res","year":"2015","unstructured":"Valli\u00e8res M, Freeman CR, Skamene SR et al (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60(14):5471\u20135496","journal-title":"Phys Med Biol"},{"issue":"1","key":"1140_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"H Zhang","year":"2019","unstructured":"Zhang H, Hung C-L, Min G et al (2019) GPU-accelerated GLRLM algorithm for feature extraction of MRI. Sci Rep 9(1):1\u201313","journal-title":"Sci Rep"},{"key":"1140_CR36","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1109\/21.44046","volume":"19","author":"MR Amadasun","year":"1989","unstructured":"Amadasun MR (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19:1264\u20131274","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"1140_CR37","unstructured":"Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis\u2014a brief tutorial. Inst Signal Inf Process 18:1\u20138"},{"issue":"1","key":"1140_CR38","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/s12984-017-0290-6","volume":"14","author":"AD Bellingegni","year":"2017","unstructured":"Bellingegni AD, Gruppioni E, Colazzo G et al (2017) NLR, MLP, SVM, and LDA: a comparative analysis on EMG data from people with trans-radial amputation. J Neuroeng Rehabil 14(1):82","journal-title":"J Neuroeng Rehabil"},{"key":"1140_CR39","doi-asserted-by":"crossref","unstructured":"Mathur A, Foody GM (2008) Multiclass and binary SVM classification: implications for training and classification users.IEEE Geosci Remot Sens Lett 5(2):241\u2013245","DOI":"10.1109\/LGRS.2008.915597"},{"issue":"1","key":"1140_CR40","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"1140_CR41","doi-asserted-by":"crossref","unstructured":"Criminisi A, Shotton J, Konukoglu E (2012) Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found Trends Comput Graph Vis 7(2\u20133):81\u2013227","DOI":"10.1561\/0600000035"},{"key":"1140_CR42","unstructured":"Tang Z, Zhao W, Xie X, Zhong Z, Shi F, Liu J, Shen D (2020) Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images. arXiv preprint. arXiv:2003.11988"},{"key":"1140_CR43","doi-asserted-by":"crossref","unstructured":"Shen C, Yu N, Cai S, Zhou J, Sheng J, Liu K, Zhou H, Guo Y, Niu G (2020) Quantitative computed tomography analysis for stratifying the severity of coronavirus disease 2019. J Pharm Anal 10(2):123\u2013129","DOI":"10.1016\/j.jpha.2020.03.004"},{"key":"1140_CR44","doi-asserted-by":"crossref","unstructured":"Shi F, Xia L, Shan F, Song B, Wu D, Wei Y, Yuan H, Jiang H, He Y, Gao Y, Sui H (2021 Mar) Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification. Phys Med Biol 17(6):065031","DOI":"10.1088\/1361-6560\/abe838"}],"container-title":["Physical and Engineering Sciences in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-022-01140-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13246-022-01140-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13246-022-01140-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,24]],"date-time":"2023-11-24T08:23:53Z","timestamp":1700814233000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13246-022-01140-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,7]]},"references-count":44,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["1140"],"URL":"https:\/\/doi.org\/10.1007\/s13246-022-01140-4","relation":{},"ISSN":["2662-4729","2662-4737"],"issn-type":[{"value":"2662-4729","type":"print"},{"value":"2662-4737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,7]]},"assertion":[{"value":"15 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"There is not any potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}