Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers.
Objective: To differentiate supratentorial single brain metastasis (MET) from glioblastoma (GBM) by using radiomic features derived from the peri-enhancing oedema region and multiple classifiers.Methods: One hundred and twenty single brain METs and GBMs were retrospectively reviewed and then randoml...
| Published in: | European Radiology Vol. 30; no. 5; pp. 3015 - 3023 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
May2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=142738685&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142738685 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: May2020 vid: 30 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142738685 142738685 NLM32006166 10.1007/s00330-019-06460-w NLM32006166 142738685 ppf: 3015 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Differentiation of supratentorial single brain metastasis and glioblastoma by using peri-enhancing oedema region-derived radiomic features and multiple classifiers. aug: au: Dong, Fei Li, Qian Jiang, Biao Zhu, Xiuliang Zeng, Qiang Huang, Peiyu Chen, Shujun Zhang, Minming affil: Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, 310009, Hangzhou, China sug: subj: Lung Neoplasms Pathology Glioma Brain Neoplasms Image Enhancement Methods Cerebral Edema Magnetic Resonance Imaging Methods Preoperative Period Adult Algorithms Aged, 80 and Over Female Young Adult Retrospective Design Male Diagnosis, Differential Random Assignment Sensitivity and Specificity Middle Age Aged Adult: 19-44 years Aged, 80 & over Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Objective: To differentiate supratentorial single brain metastasis (MET) from glioblastoma (GBM) by using radiomic features derived from the peri-enhancing oedema region and multiple classifiers.Methods: One hundred and twenty single brain METs and GBMs were retrospectively reviewed and then randomly divided into a training data set (70%) and validation data set (30%). Quantitative radiomic features of each case were extracted from the peri-enhancing oedema region of conventional MR images. After feature selection, five classifiers were built. Additionally, the combined use of the classifiers was studied. Accuracy, sensitivity, and specificity were used to evaluate the classification performance.Results: A total of 321 features were extracted, and 3 features were selected for each case. The 5 classifiers showed an accuracy of 0.70 to 0.76, sensitivity of 0.57 to 0.98, and specificity of 0.43 to 0.93 for the training data set, with an accuracy of 0.56 to 0.64, sensitivity of 0.39 to 0.78, and specificity of 0.50 to 0.89 for the validation data set. When combining the classifiers, the classification performance differed according to the combined mode and the agreement pattern of classifiers, and the greatest benefit was obtained when all the classifiers reached agreement using the same weight and simple majority vote method.Conclusions: Three features derived from the peri-enhancing oedema region had moderate value in differentiating supratentorial single brain MET from GBM with five single classifiers. Combined use of classifiers, like multi-disciplinary team (MDT) consultation, could confer extra benefits, especially for those cases when all classifiers reach agreement.Key Points: • Radiomics provides a way to differentiate single brain MET between GBM by using conventional MR images. • The results of classifiers or algorithms themselves are also data, the transformation of the primary data. • Like MDT consultation, the combined use of multiple classifiers may confer extra benefits. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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