{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:56:52Z","timestamp":1773248212477,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T00:00:00Z","timestamp":1610928000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773205"],"award-info":[{"award-number":["61773205"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1007\/s11548-021-02313-4","type":"journal-article","created":{"date-parts":[[2021,1,18]],"date-time":"2021-01-18T22:02:45Z","timestamp":1611007365000},"page":"207-217","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Survival prediction of patients suffering from glioblastoma based on two-branch DenseNet using multi-channel features"],"prefix":"10.1007","volume":"16","author":[{"given":"Xue","family":"Fu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunxiao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongsheng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,18]]},"reference":[{"key":"2313_CR1","doi-asserted-by":"publisher","first-page":"iv1","DOI":"10.1093\/neuonc\/nos218","volume":"16","author":"QT Ostrom","year":"2014","unstructured":"Ostrom QT, Gittleman H, Farah P, Ondracek A, Chen Y, Wolinsky Y, Stroup N, Kruchko C, Barnholtz-Sloan J (2014) CBTRUS statistical report: Primary brain and central nervous system tumors diagnosed in the United States in 2007\u20132011. Neuro-Oncology 16:iv1\u2013iv63. https:\/\/doi.org\/10.1093\/neuonc\/nos218","journal-title":"Neuro-Oncology"},{"key":"2313_CR2","doi-asserted-by":"publisher","first-page":"v1","DOI":"10.1093\/neuonc\/now207","volume":"18","author":"QT Ostrom","year":"2016","unstructured":"Ostrom QT, Gittleman H, Xu J, Kromer C, Wolinsky Y, Kruchko C (2016) Barnholtz-Sloan JS (2016) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009\u20132013. Neuro-oncology 18:v1\u2013v75. https:\/\/doi.org\/10.1093\/neuonc\/now207","journal-title":"Neuro-oncology"},{"issue":"6","key":"2313_CR3","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"C Kenneth","year":"2013","unstructured":"Kenneth C, Bruce V, Kirk S, John F, Justin K, Paul K, Stephen M, Stanley P, David M, Michael P, Lawrence T, Fred P (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045\u20131057. https:\/\/doi.org\/10.1007\/s10278-013-9622-7","journal-title":"J Digit Imaging"},{"issue":"12","key":"2313_CR4","doi-asserted-by":"publisher","first-page":"1680","DOI":"10.1093\/neuonc\/now086","volume":"18","author":"K Chang","year":"2016","unstructured":"Chang K, Zhang B, Guo X, Zong M, Rahman R, Sanchez D, Winder N, Reardon DA, Zhao B, Wen PY, Huang RY (2016) Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro-oncology 18(12):1680\u20131687. https:\/\/doi.org\/10.1093\/neuonc\/now086","journal-title":"Neuro-oncology"},{"key":"2313_CR5","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1007\/978-3-319-75238-9_37","volume":"10670","author":"AFI Osman","year":"2018","unstructured":"Osman AFI (2018) Automated brain tumor segmentation on magnetic resonance images and patients overall survival prediction using support vector machines. BrainLes 2017 10670:435\u2013449. https:\/\/doi.org\/10.1007\/978-3-319-75238-9_37","journal-title":"BrainLes 2017"},{"issue":"61","key":"2313_CR6","doi-asserted-by":"publisher","first-page":"104393","DOI":"10.18632\/oncotarget.22251","volume":"8","author":"C Ahmad","year":"2017","unstructured":"Ahmad C, Christian D, Matthew T, Bassam A (2017) Predicting survival time of lung cancer patients using radiomic analysis. Oncotarget 8(61):104393\u2013104407. https:\/\/doi.org\/10.18632\/oncotarget.22251","journal-title":"Oncotarget"},{"issue":"3\/4","key":"2313_CR7","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s12021-018-9377-x","volume":"16","author":"Y Xue","year":"2018","unstructured":"Xue Y, Xu T, Zhang H, Long LR, Huang X (2018) SegAN: adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 16(3\/4):383\u2013392. https:\/\/doi.org\/10.1007\/s12021-018-9377-x","journal-title":"Neuroinformatics"},{"issue":"1","key":"2313_CR8","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1109\/JBHI.2016.2635663","volume":"21","author":"A Kumar","year":"2017","unstructured":"Kumar A, Kim J, Lyndon D, Fulham M, Feng D (2017) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J Biomed Health Inform 21(1):31\u201340. https:\/\/doi.org\/10.1109\/JBHI.2016.2635663","journal-title":"IEEE J Biomed Health Inform"},{"key":"2313_CR9","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2018.2806438","author":"D Sun","year":"2018","unstructured":"Sun D, Wang M, Li A (2018) A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data. IEEE\/ACM Trans Comput Biol Bioinf. https:\/\/doi.org\/10.1109\/TCBB.2018.2806438","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2313_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0019-2","author":"GA Bello","year":"2019","unstructured":"Bello GA, Dawes TJW, Duan J, Carlo B, de Marvao A, Howard LSGE, Gibbs JSR, Wilkins MR, Cook SA, Daniel R, O\u2019Regan DP (2019) Deep learning cardiac motion analysis for human survival prediction. Nat Mach Intell. https:\/\/doi.org\/10.1038\/s42256-019-0019-2","journal-title":"Nat Mach Intell"},{"key":"2313_CR11","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.nicl.2016.10.008","volume":"13","author":"HK van der Burgh","year":"2017","unstructured":"van der Burgh HK, Schmidt R, Westeneng HJ, de Reus MA, van den Berg LH, van den Heuvel MP (2017) Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis. NeuroImage Clin 13:361\u2013369. https:\/\/doi.org\/10.1016\/j.nicl.2016.10.008","journal-title":"NeuroImage Clin"},{"key":"2313_CR12","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.1038\/s41598-018-37387-9","volume":"9","author":"D Nie","year":"2019","unstructured":"Nie D, Lu J, Zhang H, Ehsan A, Wang J, Yu Z, Liu LY, Wang Q, Wu J, Shen D (2019) Multi-channel 3D deep feature learning for survival time prediction of brain tumor patients using multi-modal neuroimages. Sci Rep 9:1103. https:\/\/doi.org\/10.1038\/s41598-018-37387-9","journal-title":"Sci Rep"},{"key":"2313_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-46718-3","author":"S Tabibu","year":"2019","unstructured":"Tabibu S, Vinod PK, Jawahar CV (2019) Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-019-46718-3","journal-title":"Sci Rep"},{"key":"2313_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243","author":"H Gao","year":"2016","unstructured":"Gao H, Zhuang L, van der Maaten L, Weinberger KQ (2016) Densely connected convolutional. Networks. https:\/\/doi.org\/10.1109\/CVPR.2017.243","journal-title":"Networks"},{"key":"2313_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093622","author":"Z Shen","year":"2019","unstructured":"Shen Z, Zhou SK, Chen Y, Georgescu B, Liu X, Huang TS (2019) One-to-one mapping for unpaired image-to-image. Translation. https:\/\/doi.org\/10.1109\/WACV45572.2020.9093622","journal-title":"Translation"},{"key":"2313_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-3679-6_8","author":"IJ Goodfellow","year":"2014","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv Neural Inf Process Syst. https:\/\/doi.org\/10.1007\/978-1-4842-3679-6_8","journal-title":"Adv Neural Inf Process Syst"},{"key":"2313_CR17","doi-asserted-by":"publisher","unstructured":"Zhu JY, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"},{"key":"2313_CR18","doi-asserted-by":"publisher","unstructured":"Isola P, Zhu JY, Zhou T, Efros AA (2017) Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. https:\/\/doi.org\/10.1109\/CVPR.2017.632","DOI":"10.1109\/CVPR.2017.632"},{"key":"2313_CR19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90","author":"K He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Comput Vis Pattern Recognit. https:\/\/doi.org\/10.1109\/CVPR.2016.90","journal-title":"Comput Vis Pattern Recognit"},{"issue":"19","key":"2313_CR20","doi-asserted-by":"publisher","first-page":"5","DOI":"10.21105\/joss.00432","volume":"2","author":"MD Bloice","year":"2017","unstructured":"Bloice MD, Stocker C, Holzinger A (2017) Augmentor: an image augmentation library for machine learning. J Open Source Softw 2(19):5\u20136. https:\/\/doi.org\/10.21105\/joss.00432","journal-title":"J Open Source Softw"},{"issue":"10","key":"2313_CR21","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber M-A, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993\u20132024. https:\/\/doi.org\/10.1109\/TMI.2014.2377694","journal-title":"IEEE Trans Med Imaging"},{"key":"2313_CR22","doi-asserted-by":"publisher","first-page":"170117","DOI":"10.1038\/sdata.2017.117","volume":"4","author":"S Bakas","year":"2017","unstructured":"Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C (2017) Advancing the Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat Sci Data 4:170117. https:\/\/doi.org\/10.1038\/sdata.2017.117","journal-title":"Nat Sci Data"},{"key":"2313_CR23","doi-asserted-by":"publisher","unstructured":"Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara R, Berger C, Ha S, Rozycki M, Prastawa M, Alberts E, Lipkova J, Freymann J, Kirby J, Bilello M, Fathallah-Shaykh H, Wiest R, Kirschke J (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. https:\/\/doi.org\/10.17863\/CAM.38755","DOI":"10.17863\/CAM.38755"},{"issue":"4","key":"2313_CR24","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612. https:\/\/doi.org\/10.1109\/TIP.2003.819861","journal-title":"IEEE Trans Image Process"},{"key":"2313_CR25","doi-asserted-by":"publisher","unstructured":"Godbole S, Sarawagi S (2004) Discriminative methods for multi-labeled classification. In: 8th Pacific\/Asia conference on advances in knowledge discovery and data. https:\/\/doi.org\/10.1007\/978-3-540-24775-3_5","DOI":"10.1007\/978-3-540-24775-3_5"},{"issue":"8","key":"2313_CR26","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861\u2013874. https:\/\/doi.org\/10.1016\/j.patrec.2005.10.010","journal-title":"Pattern Recognit Lett"},{"issue":"1","key":"2313_CR27","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, Mcneil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29\u201336. https:\/\/doi.org\/10.1148\/radiology.143.1.7063747","journal-title":"Radiology"},{"issue":"402","key":"2313_CR28","doi-asserted-by":"publisher","first-page":"414","DOI":"10.2307\/2288857","volume":"83","author":"B Efron","year":"1988","unstructured":"Efron B (1988) Logistic regression, survival analysis, and the Kaplan-Meier curve. J Am Stat Assoc 83(402):414\u2013425. https:\/\/doi.org\/10.2307\/2288857","journal-title":"J Am Stat Assoc"},{"issue":"2","key":"2313_CR29","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1145\/3065386","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25(2):1097\u20131105. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Adv Neural Inf Process Syst"},{"key":"2313_CR30","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. Comput Sci. https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"2313_CR31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324","author":"T-Y Lin","year":"2017","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Dollar P (2017) Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/ICCV.2017.324","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2313_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-28954-6","author":"W Samek","year":"2019","unstructured":"Samek W, Montavon G, Vedaldi A, Hansen L, M\u00fcller K-R (2019) Explainable AI: Interpreting, Explaining And Visualizing deep. Learning. https:\/\/doi.org\/10.1007\/978-3-030-28954-6","journal-title":"Learning"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02313-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11548-021-02313-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02313-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,12]],"date-time":"2021-02-12T12:10:06Z","timestamp":1613131806000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11548-021-02313-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,18]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2021,2]]}},"alternative-id":["2313"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02313-4","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,18]]},"assertion":[{"value":"5 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}