Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis

Omar Kouli; Ahmed Hassane; Dania Badran; Tasnim Kouli; Kismet Hossain-Ibrahim; J Douglas Steele

doi:10.1093/noajnl/vdac081

Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis

Neurooncol Adv. 2022 May 27;4(1):vdac081. doi: 10.1093/noajnl/vdac081. eCollection 2022 Jan-Dec.

Authors

Omar Kouli¹, Ahmed Hassane², Dania Badran², Tasnim Kouli¹, Kismet Hossain-Ibrahim³, J Douglas Steele⁴

Affiliations

¹ School of Medicine, University of Dundee, Dundee, UK.
² NHS Greater Glasgow and Clyde, Glasgow, UK.
³ Division of Neurosurgery, NHS Tayside, Dundee, UK.
⁴ Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee, UK.

Abstract

Background: Automated brain tumor identification facilitates diagnosis and treatment planning. We evaluate the performance of traditional machine learning (TML) and deep learning (DL) in brain tumor detection and segmentation, using MRI.

Methods: A systematic literature search from January 2000 to May 8, 2021 was conducted. Study quality was assessed using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Detection meta-analysis was performed using a unified hierarchical model. Segmentation studies were evaluated using a random effects model. Sensitivity analysis was performed for externally validated studies.

Results: Of 224 studies included in the systematic review, 46 segmentation and 38 detection studies were eligible for meta-analysis. In detection, DL achieved a lower false positive rate compared to TML; 0.018 (95% CI, 0.011 to 0.028) and 0.048 (0.032 to 0.072) (P < .001), respectively. In segmentation, DL had a higher dice similarity coefficient (DSC), particularly for tumor core (TC); 0.80 (0.77 to 0.83) and 0.63 (0.56 to 0.71) (P < .001), persisting on sensitivity analysis. Both manual and automated whole tumor (WT) segmentation had "good" (DSC ≥ 0.70) performance. Manual TC segmentation was superior to automated; 0.78 (0.69 to 0.86) and 0.64 (0.53 to 0.74) (P = .014), respectively. Only 30% of studies reported external validation.

Conclusions: The comparable performance of automated to manual WT segmentation supports its integration into clinical practice. However, manual outperformance for sub-compartmental segmentation highlights the need for further development of automated methods in this area. Compared to TML, DL provided superior performance for detection and sub-compartmental segmentation. Improvements in the quality and design of studies, including external validation, are required for the interpretability and generalizability of automated models.

Keywords: artificial intelligence; brain tumor; machine learning; meta-analysis; segmentation.