Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.
Objective: We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features.Methods: A total of 95 patients were divided into a training...
| Publicado en: | European Radiology Vol. 29; no. 4; pp. 1841 - 1848 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
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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=135371927&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135371927 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Apr2019 vid: 29 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135371927 135371927 NLM30280245 135371927 10.1007/s00330-018-5730-6 NLM30280245 135371927 ppf: 1841 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features. aug: au: Yin, Ping Mao, Ning Zhao, Chao Wu, Jiangfen Sun, Chao Chen, Lei Hong, Nan affil: Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, 100044, Beijing, People's Republic of China sug: subj: Giant Cell Tumors Spinal Neoplasms Sacrum Imaging, Three-Dimensional Neoplasms, Germ Cell and Embryonal Tomography, X-Ray Computed Image Interpretation, Computer Assisted Methods Linear Regression Male Retrospective Design Diagnosis, Differential Female ROC Curve Adult Middle Age Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objective: We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features.Methods: A total of 95 patients were divided into a training set and a validation set. Three best feature selection methods (Relief, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF)) and three classification methods, including generalised linear models (GLM), support vector machines (SVM) and RF, were compared for their performance in distinguishing SC and SGCT. The performance of the radiomics model was investigated via area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) analysis.Results: The selection method LASSO + classifier GLM had the highest AUC of 0.984 and ACC of 0.897 in the validating set, followed by Relief + GLM (AUC = 0.909, ACC = 0.862) and LASSO + SVM (AUC = 0.900, ACC = 0.862) based on CTE features. For CT features, RF + GLM had the highest AUC of 0.889, while LASSO + GLM achieved a high ACC of 0.793 in the validating set. Regardless of the methods, CTE features significantly outperformed those from CT for the differentiation of SC and SGCT (ZAUC = -3.029, ZACC = -4.553; p < 0.05).Conclusions: Our study demonstrated CTE features performed better than CT features. The selection method LASSO + classifier GLM had the best performance in differentiation of SC and SGCT, which could enhance the application of radiomics methods in sacral tumours.Key Points: • Sacral chordoma and sacral giant cell tumour are the two most common primary tumours of the sacrum with many common clinical and imaging characteristics. • A radiomics model helps clinicians to identify the histology of a sacral tumour. • CTE features should be preferred. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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