Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features.
Objective: To differentiate brain pilocytic astrocytoma (PA) from glioblastoma (GBM) using contrast-enhanced magnetic resonance imaging (MRI) quantitative radiomic features by a decision tree model.Methods: Sixty-six patients from two centres (PA, n = 31; GBM, n = 35) were randomly divided into trai...
| Publicado en: | European Radiology Vol. 29; no. 8; pp. 3968 - 3976 |
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| Autores principales: | , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137304074&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137304074 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Aug2019 vid: 29 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137304074 137304074 NLM30421019 137304074 10.1007/s00330-018-5706-6 NLM30421019 137304074 ppf: 3968 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Differentiation between pilocytic astrocytoma and glioblastoma: a decision tree model using contrast-enhanced magnetic resonance imaging-derived quantitative radiomic features. aug: au: Dong, Fei Li, Qian Xu, Duo Xiu, Wenji Zeng, Qiang Zhu, Xiuliang Xu, Fangfang Jiang, Biao Zhang, Minming affil: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China sug: subj: Glioma Diagnosis Decision Trees Brain Pathology Brain Neoplasms Diagnosis Magnetic Resonance Imaging Methods Algorithms Middle Age Female Image Enhancement Retrospective Design Human Diagnosis, Differential Child Aged Male Adolescence Young Adult Adult Validation Studies Comparative Studies Evaluation Research Multicenter Studies Middle Aged: 45-64 years Child: 6-12 years Aged: 65+ years Adolescent: 13-18 years Adult: 19-44 years Female Male ab: Objective: To differentiate brain pilocytic astrocytoma (PA) from glioblastoma (GBM) using contrast-enhanced magnetic resonance imaging (MRI) quantitative radiomic features by a decision tree model.Methods: Sixty-six patients from two centres (PA, n = 31; GBM, n = 35) were randomly divided into training and validation data sets (about 2:1). Quantitative radiomic features of the tumours were extracted from contrast-enhanced MR images. A subset of features was selected by feature stability and Boruta algorithm. The selected features were used to build a decision tree model. Predictive accuracy, sensitivity and specificity were used to assess model performance. The classification outcome of the model was combined with tumour location, age and gender features, and multivariable logistic regression analysis and permutation test using the entire data set were performed to further evaluate the decision tree model.Results: A total of 271 radiomic features were successfully extracted for each tumour. Twelve features were selected as input variables to build the decision tree model. Two features S(1, -1) Entropy and S(2, -2) SumAverg were finally included in the model. The model showed an accuracy, sensitivity and specificity of 0.87, 0.90 and 0.83 for the training data set and 0.86, 0.80 and 0.91 for the validation data set. The classification outcome of the model related to the actual tumour types and did not rely on the other three features (p < 0.001).Conclusions: A decision tree model with two features derived from the contrast-enhanced MR images performed well in differentiating PA from GBM.Key Points: • MRI findings of PA and GBM are sometimes very similar. • Radiomics provides much more quantitative information about tumours. • Radiomic features can help to distinguish PA from GBM. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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