Kouli, Omar, Hassane, Ahmed, Badran, Dania 
ORCID: 0000-0002-6114-1355, Kouli, Tasnim, Hossain-Ibrahim, Kismet and Steele, J Douglas
(2022)
Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis.
NEURO-ONCOLOGY ADVANCES, 4 (1).
vdac081-.
Abstract
<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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) (<i>P</i> < .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) (<i>P</i> < .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) (<i>P</i> = .014), respectively. Only 30% of studies reported external validation.<h4>Conclusions</h4>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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence, brain tumor, machine learning, meta-analysis, segmentation |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 14 Dec 2023 09:01 |
| Last Modified: | 14 Dec 2023 09:07 |
| DOI: | 10.1093/noajnl/vdac081 |
| Open Access URL: | https://doi.org/10.1093/noajnl/vdac081 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3177370 |
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