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

Simple Summary Manual detection and delineation of brain metastases are time consuming and variable. Studies have therefore been conducted to automate this process using imaging studies and artificial intelligence. To the best of our knowledge, no study has conducted a systematic review and meta-analysis on brain metastasis detection using only deep learning and MRI. As a result, a systematic review of this topic is required, as well as an assessment of the quality of the studies and a meta-analysis to determine the strength of the current evidence. The purpose of this study was to perform a systematic review and meta-analysis of the performance of deep learning models that use MRI to detect brain metastases in cancer patients. 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.