{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T10:16:55Z","timestamp":1778494615935,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:00:00Z","timestamp":1575331200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T00:00:00Z","timestamp":1575331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s10278-019-00290-4","type":"journal-article","created":{"date-parts":[[2019,12,3]],"date-time":"2019-12-03T17:19:59Z","timestamp":1575393599000},"page":"391-398","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Comparison of the Efficiency of Using a Deep CNN Approach with Other Common Regression Methods for the Prediction of EGFR Expression in Glioblastoma Patients"],"prefix":"10.1007","volume":"33","author":[{"given":"Mohammadreza","family":"Hedyehzadeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0980-0154","authenticated-orcid":false,"given":"Keivan","family":"Maghooli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"MomenGharibvand","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Pistorius","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,3]]},"reference":[{"key":"290_CR1","doi-asserted-by":"publisher","first-page":"277","DOI":"10.3322\/caac.20073","volume":"60","author":"A Jemal","year":"2017","unstructured":"Jemal A, Siegel R, Xu J, Ward E: Cancer Statistics. CA Cancer J Clin 60:277\u2013300, 2017","journal-title":"CA Cancer J Clin"},{"key":"290_CR2","unstructured":"International Radiosurgery Association Glioblastomas. Available via http:\/\/www.irsa.org\/glioblastoma.html. Accessed 19 Dec 2018"},{"key":"290_CR3","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1056\/NEJMoa043330","volume":"10","author":"R Stupp","year":"2005","unstructured":"Stupp R, Weller M, Belanger K et al.: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 10:987\u2013996, 2005","journal-title":"N Engl J Med"},{"key":"290_CR4","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.3174\/ajnr.A3604","volume":"34","author":"RJ Young","year":"2013","unstructured":"Young RJ, Gupta A, Shah AD et al.: Potential role of preoperative conventional MRI including diffusion measurements in assessing epidermal growth factor receptor gene amplification status in patients with glioblastoma. AJNR Am J Neuroradiol 34:2271\u20132277, 2013","journal-title":"AJNR Am J Neuroradiol"},{"key":"290_CR5","doi-asserted-by":"publisher","first-page":"729","DOI":"10.1259\/bjr\/83796755","volume":"85","author":"K Rock","year":"2012","unstructured":"Rock K, Mcardle O, Forde P et al.: A clinical review of treatment outcomes in glioblastoma multiforme--the validation in a non-trial population of the results of a randomised Phase III clinical trial: has a more radical approach improved survival? Br J Radiol 85:729\u2013733, 2012","journal-title":"Br J Radiol"},{"key":"290_CR6","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1111\/j.1750-3639.1993.tb00752.x","volume":"3","author":"P Kleihues","year":"1993","unstructured":"Kleihues P, Burger PC, Scheithauer BW: The new WHO classification of brain tumours. Brain Pathol Zurich Switz 3:255\u2013268, 1993","journal-title":"Brain Pathol Zurich Switz"},{"key":"290_CR7","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.acra.2009.11.001","volume":"17","author":"SC Davis","year":"2010","unstructured":"Davis SC, Samkoe KS, O\u2019Hara JA et al.: MRI-coupled fluorescence tomography quantifies EGFR activity in brain tumors. Acad Radiol 17:271\u2013276, 2010","journal-title":"Acad Radiol"},{"key":"290_CR8","doi-asserted-by":"publisher","first-page":"600","DOI":"10.4161\/cbt.19849","volume":"13","author":"M Ramos-Suzarte","year":"2012","unstructured":"Ramos-Suzarte M, Lorenzo-Luaces P, Lazo NG et al.: Treatment of malignant, non-resectable, epithelial origin esophageal tumours with the humanized anti-epidermal growth factor antibody nimotuzumab combined with radiation therapy and chemotherapy. Cancer Biol Ther 13:600\u2013605, 2012","journal-title":"Cancer Biol Ther"},{"key":"290_CR9","doi-asserted-by":"publisher","unstructured":"Beig N, Patel J, Prasanna P, et al: Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma. DOI:https:\/\/doi.org\/10.1117\/12.2255694, 2017","DOI":"10.1117\/12.2255694"},{"key":"290_CR10","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1148\/radiol.2017161845","volume":"286","author":"M Zhou","year":"2018","unstructured":"Zhou M, Leung A, Echegaray S et al.: Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology 286:307\u2013315, 2018","journal-title":"Radiology"},{"key":"290_CR11","doi-asserted-by":"crossref","unstructured":"Zhu Z, Albadawy E, Saha A, et al: Deep learning for identifying radiogenomic associations in breast cancer.Computers in biology and medicine, 109, 85\u201390, Cs, 2019","DOI":"10.1016\/j.compbiomed.2019.04.018"},{"key":"290_CR12","doi-asserted-by":"publisher","unstructured":"Jeong WK, Jamshidi N, Felker ER, et al: Radiomics and radiogenomics of primary liver cancers. DOI:https:\/\/doi.org\/10.3350\/cmh.2018.1007, 2018","DOI":"10.3350\/cmh.2018.1007"},{"key":"290_CR13","doi-asserted-by":"publisher","first-page":"8600","DOI":"10.1158\/1078-0432.CCR-05-0713","volume":"11","author":"M Aghi","year":"2005","unstructured":"Aghi M: Magnetic resonance imaging characteristics predict epidermal growth factor receptor amplification status in glioblastoma. Clin Cancer Res 11:8600\u20138605, 2005","journal-title":"Clin Cancer Res"},{"key":"290_CR14","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s00062-014-0289-3","volume":"25","author":"A Gupta","year":"2015","unstructured":"Gupta A, Young RJ, Shah AD et al.: Pretreatment dynamic susceptibility contrast MRI perfusion in glioblastoma: prediction of EGFR gene amplification. Clin Neuroradiol 25:143\u2013150, 2015","journal-title":"Clin Neuroradiol"},{"key":"290_CR15","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/s00330-017-4964-z","volume":"28","author":"Y Li","year":"2018","unstructured":"Li Y, Liu X, Xu K et al.: MRI features can predict EGFR expression in lower grade gliomas: a voxel-based radiomic analysis. Eur Radiol 28:356\u2013362, 2018","journal-title":"Eur Radiol"},{"key":"290_CR16","doi-asserted-by":"publisher","unstructured":"Chen L, Zhang H, Lu J et al.: Multi-label nonlinear matrix completion with transductive multi-task feature selection for joint MGMT and IDH1 status prediction of patient with high-grade gliomas. IEEE Trans Med Imaging, 2018. https:\/\/doi.org\/10.1109\/TMI.2018.2807590","DOI":"10.1109\/TMI.2018.2807590"},{"key":"290_CR17","doi-asserted-by":"publisher","unstructured":"Gevaert O, Echegaray S, Khuong A et al.: Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep, 2017. https:\/\/doi.org\/10.1038\/srep41674","DOI":"10.1038\/srep41674"},{"key":"290_CR18","doi-asserted-by":"crossref","unstructured":"Yu D, Zhou M, Yang F, Dong D, Gevaert O, Liu Z, Shi J, Tian J: Conolutional neural networks for predicting molecular profiles of non small cell lung cancer. In 2017 IEEE 14th International Symposium on Biomedical Imaging, (pp. 569\u2013572)","DOI":"10.1109\/ISBI.2017.7950585"},{"key":"290_CR19","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.crad.2019.01.028","volume":"74","author":"P Korfiatis","year":"2019","unstructured":"Korfiatis P, Erickson B: Deep learning can see the unseeable: predicting molecular markers from MRI of brain gliomas. J Clin Radiol 74:367\u2013373, 2019","journal-title":"J Clin Radiol"},{"key":"290_CR20","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1038\/nature07385","volume":"455","author":"L Chin","year":"2008","unstructured":"Chin L, Meyerson M, Aldape K, Bigner D: Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455:1061\u20131068, 2008","journal-title":"Nature"},{"key":"290_CR21","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 26:1045\u20131057, 2013","journal-title":"J Digit Imaging"},{"key":"290_CR22","doi-asserted-by":"publisher","unstructured":"Colen RR, Vangel M, Wang J et al.: Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project. BMC Med Genomics, 2014. https:\/\/doi.org\/10.1186\/1755-8794-7-30","DOI":"10.1186\/1755-8794-7-30"},{"key":"290_CR23","doi-asserted-by":"publisher","unstructured":"Jamshidi N, Diehn M, Bredel M, Kuo MD: Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation. Radiology, 2014. https:\/\/doi.org\/10.1148\/radiol.13130078","DOI":"10.1148\/radiol.13130078"},{"key":"290_CR24","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1148\/radiol.14131731","volume":"273","author":"O Gevaert","year":"2014","unstructured":"Gevaert O, Mitchell LA, Achrol AS et al.: Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273:168\u2013174, 2014","journal-title":"Radiology"},{"key":"290_CR25","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1007\/s10278-017-9984-3","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus Z, Ali I, Sedl\u00e1\u0159 J et al.: Predicting deletion of chromosomal arms 1p\/19q in low-grade gliomas from MR images using machine intelligence. J Digit Imaging 30:469\u2013476, 2017","journal-title":"J Digit Imaging"},{"key":"290_CR26","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/s10278-017-0009-z","volume":"30","author":"P Korfiatis","year":"2017","unstructured":"Korfiatis P, Kline TL, Lachance DH et al.: Residual deep convolutional neural network predicts MGMT methylation status. J Digit Imaging 30:622\u2013628, 2017","journal-title":"J Digit Imaging"},{"key":"290_CR27","doi-asserted-by":"publisher","unstructured":"Wang G, Li W, Ourselin S, Vercauteren T: Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. DOI:https:\/\/doi.org\/10.1007\/978-3-319-75238-9_16, 2018","DOI":"10.1007\/978-3-319-75238-9_16"},{"key":"290_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Rudnicki WR: Feature selection with the Boruta package. J Stat Softw 36:1\u201313, 2010","journal-title":"J Stat Softw"},{"key":"290_CR29","unstructured":"Choosing the right estimator-scikit-learn 0.20.2 documentation. Available via https:\/\/scikit-learn.org\/stable\/tutorial\/machine_learning_map\/index.html. Accessed 26 Dec 2018"},{"key":"290_CR30","unstructured":"Liu W, Lin R, Yang M: Robust elastic net regression. ArXiv151104690 Cs Stat, 2015"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00290-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-019-00290-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00290-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T09:46:13Z","timestamp":1778492773000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-019-00290-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,3]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["290"],"URL":"https:\/\/doi.org\/10.1007\/s10278-019-00290-4","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,3]]},"assertion":[{"value":"3 December 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}