Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis
Open Access
- 2 March 2021
- journal article
- review article
- Published by Springer Science and Business Media LLC in Neuroradiology
- Vol. 63 (8), 1293-1304
- https://doi.org/10.1007/s00234-021-02668-0
Abstract
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.Funding Information
- Università degli Studi di Napoli Federico II
This publication has 59 references indexed in Scilit:
- The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summaryActa Neuropathologica, 2016
- A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability ResearchJournal of Chiropractic Medicine, 2016
- Radiomics: Images Are More than Pictures, They Are DataRadiology, 2016
- Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: A discussion and proposal for an alternative approachEuropean Radiology, 2015
- Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-AnalysisKorean Journal of Radiology, 2015
- Management of Petroclival Meningiomas: A Review of the Development of Current TherapyJournal of Neurological Surgery Part B: Skull Base, 2014
- QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy StudiesAnnals of Internal Medicine, 2011
- Assessing the Performance of Prediction ModelsEpidemiology, 2010
- Meta-AnalysisCirculation, 2007
- Measuring inconsistency in meta-analysesBMJ, 2003