Application of radiomics to meningiomas: A systematic review

Abstract

Background: Quantitative imaging analysis through radiomics is a powerful technology to non-invasively assess molecular correlates and guide clinical decision-making. There has been growing interest in image-based phenotyping for meningiomas given the complexities in management.

Methods: We systematically reviewed meningioma radiomics analyses published in PubMed, Embase, and Web of Science until December 20, 2021. We compiled performance data and assessed publication quality using the radiomics quality score (RQS).

Results: A total of 170 publications were grouped into 5 categories of radiomics applications to meningiomas: Tumor detection and segmentation (21%), classification across neurologic diseases (54%), grading (14%), feature correlation (3%), and prognostication (8%). A majority focused on technical model development (73%) versus clinical applications (27%), with increasing adoption of deep learning. Studies utilized either private institutional (50%) or public (49%) datasets, with only 68% using a validation dataset. For detection and segmentation, radiomic models had a mean accuracy of 93.1 ± 8.1% and a dice coefficient of 88.8 ± 7.9%. Meningioma classification had a mean accuracy of 95.2 ± 4.0%. Tumor grading had a mean area-under-the-curve (AUC) of 0.85 ± 0.08. Correlation with meningioma biological features had a mean AUC of 0.89 ± 0.07. Prognostication of the clinical course had a mean AUC of 0.83 ± 0.08. While clinical studies had a higher mean RQS compared to technical studies, quality was low overall with a mean RQS of 6.7 ± 5.9 (possible range -8 to 36).

Conclusions: There has been global growth in meningioma radiomics, driven by data accessibility and novel computational methodology. Translatability toward complex tasks such as prognostication requires studies that improve quality, develop comprehensive patient datasets, and engage in prospective trials.

Keywords: artificial intelligence; cancer phenotype; genomics; meningioma; radiomics.

Figure 1.

(A) Preferred reporting items for systematic reviews and meta-analyses flowchart of study design. (B) Applications of radiomics to meningiomas. (C) Number of experiments per year, subdivided by type of analysis. (D) Global heatmap of publications on meningioma radiomics.

Figure 2.

(A) Imaging datasets used in included experiments. (B) Global heatmap of imaging datasets, shading reflects fraction of public versus institutional datasets used (Institutional: single and multi-center). (C) Imaging modalities used—T1 (64%): T1, T1C, MP-RAGE, T1-Subtraction, T2 (27%): T2, fluid attenuated inversion recovery, gradient recalled echo, Diffusion (8%): diffusion weighted image, apparent diffusion coefficient, diffusion tensor image, diffusion kurtosis image, Other (1%): CT, proton density, susceptibility weighted image. (D) Meningioma images and patient cases per study (2 experiments used a combined public/institutional dataset—binned as public or institutional based on which dataset contributed a larger number of images). (E) Statistical and analytical models used. (F) Distribution of clinical versus technical experiments, datasets, and models used.

Figure 3.

(A) Clinical and technical experiments over time. (B) Citations per year of clinical and technical publications. (C) Reported statistical data across meningioma radiomics categories (only studies with meningioma specific data included), P < .05 (*), P < .01 (**).

Figure 4.

(A) Radiomics Quality Score (RQS) by study class (institutional data only for grading, feature correlation, and prognostication). (B) RQS for clinical versus technical studies, P < .05 (*). (C) RQS breakdown by percent of points earned in each category.