Due to different phenotypes of tumors, tumor cell subsets are not consistent in terms of cell proliferation rate, morphology, karyotype, cell surface markers (antigenicity), biochemical metabolism, and invasion and metastasis ability. Imaging features were selected through high-throughput computers, and target data were classified using mathematical models to explore the correlation between imaging features and tumors at the cellular and molecular levels, which is currently a hot topic in medical research. Based on this, the concept of radiomics emerged in 2012(33).The concept of radiomics is the comprehensive analysis of features using tools, such as MaZda, MATLAB, TexRAD, MISSTA, CAD, and FireVoxel, which can provide new quantitative imaging markers without the need for new acquisition devices or tracers and has stronger diagnostic capabilities than ordinary inspection methods. To systematically evaluate the accuracy of radiomics in distinguishing HGM and LGM, the author conducted this meta-analysis. The results showed that the combined SEN and SPE of MRI radiomics were 0.82 and 0.85, respectively, and PLR and NLR were 5.64 and 0.21, respectively. The AUC was 0.91, which indicated that MRI radiomics has high diagnostic value in the grading of meningiomas. Previous meta-analyses (34) systematically reviewed and evaluated the methodological quality of studies using radiomics for the diagnosis and prediction of intracranial meningiomas. The overall quality score and percentage quality score of radiomics have moderate to good inter-reader reproducibility. This meta-analysis showed an overall AUC of 0.88 (95% CI : 0.84–0.93) with a standard error of 0.02. The AUC value in this study was 0.91 (95% CI: 0.88–0.93), surpassing that of previous studies. The primary focus of this study was the classification of HGM and LGM based on MRI radiomics. Importantly, this study not only complemented prior research but also underscored the significance of MRI radiomics.
Compared with WHO grade 1 meningiomas, HGM are potentially malignant, with a high risk of local recurrence, poor prognosis, and low overall survival rate. Therefore, the accurate preoperative prediction of meningioma recurrence and brain invasion becomes extremely important. According to a multi-center study(35), a low spherical shape of meningiomas is associated with a high local recurrence rate and low survival rate. The RF model, which integrates image-based features with clinical features, demonstrates the highest predictive performance, with an AUC of 0.78. However, due to the inclusion of both primary and recurrent meningiomas in the study, there exists relative heterogeneity. Another study (36) utilized a SVM prediction model based on T1WI(T1-weighted imaging) enhancement and T2WI(T2-weighted imaging) sequences to foresee the potential of meningioma invasion before surgery. The research results highlighted that the AUC reached as high as 0.82. Wang L et al. (37) employed MRI radiomics features to develop a model based on preoperative contrast-enhanced T1WI and T2WI magnetic resonance imaging aiming to precisely predict meningioma venous sinus infiltration before surgery.The areas under the curve for the training and validation groups were 0.86 and 0.82, respectively, which is helpful for selecting surgical approaches and predicting prognosis.In summary, precise preoperative prediction of meningioma grading holds significant importance.
The included literature used a variety of MRI sequences, and functional imaging sequences can provide more information on physiology, hemodynamics, and other information that conventional sequences cannot provide. However, unconventional sequences are not widely used in clinical practice as conventional MRI sequences. Image-based radiomics data that were extracted from conventional MRI seems to be a more practical and direct solution. How to standardize image-based radiomics in conventional sequences may help its clinical application.
The entire process of image-based radiomics research involves repeatability and quality control, including data acquisition, feature extraction and selection, modeling, and validation. Particularly, patient inclusion and exclusion criteria can lead to different usability analyses for different populations, especially for small cohort studies. Therefore, it is necessary to adopt standardized inclusion and exclusion criteria and use open dataset to proceed model validation and improve the reproducibility and quality control of radiomics research (38).76.2% (16/21) of the literature included in this article underwent validation, enhancing the reproducibility and quality control of MRI radiomics and mitigating the risk of false positives and overly optimistic results. Currently, the application of MRI radiomics in meningiomas is still in its initial stage. The small sample size involved in most studies may lead to overfitting of the model. What’s more, 57.1% (12/21) of the studies included in this article took measures to avoid overfitting.In the future, additional prospective, multi-center, large-scale studies are required to enhance the applicability of findings across diverse populations.
To explore the sources of heterogeneity, subgroup analysis was conducted in this study. There was no significant difference, highlighting the results of this study as reliable and highly representative. However, this study has certain limitations. Firstly:Most of the studies included in this analysis were retrospective (20/21), potentially introducing selection bias. Therefore, future research endeavors should be prospectively designed and implemented to enhance overall research quality. Secondly, Although MRI radiomics models helped differentiate the diagnosis of HGM and LGM, they involved many analytical methods. Therefore, the choice of modeling methods may affect the predictive results of MRI radiomics analysis, thus leading to heterogeneity. Thirdly,The heterogeneity of included literature was high, and the source of heterogeneity was not found through subgroup analysis. Only a preliminary analysis of the sources of heterogeneity was conducted. Fourthly,Some studies were designed as single-center studies and lacked external verification, which may result in low reproducibility of MRI radiomics characteristics.