A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiform...

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Veröffentlicht in:	Scientific reports 2017-09, Vol.7 (1), p.10353-8, Article 10353
Hauptverfasser:	Lao, Jiangwei, Chen, Yinsheng, Li, Zhi-Cheng, Li, Qihua, Zhang, Ji, Liu, Jing, Zhai, Guangtao
Format:	Artikel
Sprache:	eng
Schlagworte:	59/57 631/114/1305 631/114/1564 639/166/985 Adolescent Adult Aged Aged, 80 and over Algorithms Archives & records Brain cancer Cancer Child Datasets Deep Learning Edema Feature selection Female Geometry Glioblastoma Glioblastoma - diagnostic imaging Glioblastoma - mortality Humanities and Social Sciences Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Kaplan-Meier Estimate Magnetic Resonance Imaging - methods Male Medical imaging Medical prognosis Middle Aged Models, Theoretical multidisciplinary Neural networks Nomograms Oncology Open source software Patients Prognosis Public domain Radiomics Reproducibility of Results Risk factors ROC Curve Science Science (multidisciplinary) Survival Transfer learning Young Adult
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