Abstract
PurposeTo 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.MethodsArticles 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.ResultsIn 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.ConclusionsMachine 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.
Highlights
Meningiomas are the most common primary intracranial tumor in adults, being more frequent in middle-aged women [1]
The search terms consisted of machine learning OR artificial intelligence OR radiomics OR texture AND meningioma; the detailed search string is presented in the supplementary materials
23 articles were included in the systematic review, 8 of which were eligible for the meta-analysis
Summary
Meningiomas are the most common primary intracranial tumor in adults, being more frequent in middle-aged women [1]. The term radiomics includes different quantitative radiological image analysis techniques, ranging from first order statistics to texture analysis [6]. These produce large amounts of data that can be challenging to process with classical statistical methods but may contribute novel imaging biomarkers. Among the promises of ML for clinical practice, there are automatic detection and characterization of lesions and the possibility to predict response to therapy and risk of recurrence [11,12,13] Regarding neuroradiology, it has shown good results in different applications, especially in the field of neuro-oncology [14,15,16]. The number of investigations based on these techniques published allows for data pooling potentially achieving higher levels of evidence through systematic reviews and/or meta-analyses
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