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Review
. 2023 Jan 4;15(2):334.
doi: 10.3390/cancers15020334.

Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis

Burak B Ozkara  1 , Melissa M Chen  1 , Christian Federau  2 , Mert Karabacak  3 , Tina M Briere  4 , Jing Li  5 , Max Wintermark  1
Affiliations
Review

Deep Learning for Detecting Brain Metastases on MRI: A Systematic Review and Meta-Analysis

Burak B Ozkara et al. Cancers (Basel). .

Abstract

Since manual detection of brain metastases (BMs) is time consuming, studies have been conducted to automate this process using deep learning. The purpose of this study was to conduct a systematic review and meta-analysis of the performance of deep learning models that use magnetic resonance imaging (MRI) to detect BMs in cancer patients. A systematic search of MEDLINE, EMBASE, and Web of Science was conducted until 30 September 2022. Inclusion criteria were: patients with BMs; deep learning using MRI images was applied to detect the BMs; sufficient data were present in terms of detective performance; original research articles. Exclusion criteria were: reviews, letters, guidelines, editorials, or errata; case reports or series with less than 20 patients; studies with overlapping cohorts; insufficient data in terms of detective performance; machine learning was used to detect BMs; articles not written in English. Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging was used to assess the quality. Finally, 24 eligible studies were identified for the quantitative analysis. The pooled proportion of patient-wise and lesion-wise detectability was 89%. Articles should adhere to the checklists more strictly. Deep learning algorithms effectively detect BMs. Pooled analysis of false positive rates could not be estimated due to reporting differences.

Keywords: artificial intelligence; brain metastasis; deep learning; magnetic resonance imaging; pooled analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Meta-analysis design.
Figure 2
Figure 2
Study selection process.
Figure 3
Figure 3
A summary of the Quality Assessment of Diagnostic Accuracy Studies-2 results.
Figure 4
Figure 4
Forest plot of deep learning algorithms’ patient-wise detectability.
Figure 5
Figure 5
Funnel plot for studies included in the pooled analysis for patient-wise detectability.
Figure 6
Figure 6
Forest plot of deep learning algorithms’ lesion-wise detectability.
Figure 7
Figure 7
Funnel plot for studies included in the pooled analysis for lesion-wise detectability.
See this image and copyright information in PMC

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