Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.
Purpose: To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-...
| Publicado en: | Neuroradiology Vol. 63; no. 8; pp. 1293 - 1305 |
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| Autores principales: | , , , , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2021
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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=151508359&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151508359 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Aug2021 vid: 63 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 151508359 148992693 151508359 151508359 10.1007/s00234-021-02668-0 151508359 ppf: 1293 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis. aug: au: Ugga, Lorenzo Perillo, Teresa Cuocolo, Renato Stanzione, Arnaldo Romeo, Valeria Green, Roberta Cantoni, Valeria Brunetti, Arturo affil: Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy sug: subj: Meningioma Diagnosis Magnetic Resonance Imaging Machine Learning Meningioma Prognosis Human Systematic Review Meta Analysis Confidence Intervals Descriptive Statistics ab: Purpose: To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods: Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results: In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions: Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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