TITLE:
Deep Learning for Detecting Brain Tumors on MRI: Systematic Review, Diagnostic Accuracy Meta-Analysis, and Assessment of Data Report Completeness (2019-2024)
AUTHORS:
Gedeon Nitiema, James T. Kouawa, Ahmed Dooguy Kora
KEYWORDS:
Brain Tumor, Magnetic Resonance Imaging, Deep Learning, Diagnostic Accuracy, Systematic Review, Meta-Analysis, Report Completeness
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.10,
October
13,
2026
ABSTRACT: Detecting brain tumors in MRI images is a major clinical challenge, as patient outcomes depend on how early the diagnosis is made. Deep learning has sparked a considerable number of publications over the past six years, but no quantitative summary of diagnostic accuracy has been conducted for the period 2019-2024. Our goal was to estimate the pooled diagnostic performance of deep learning models applied to brain tumor detection in MRI, and to assess how thoroughly diagnostic accuracy data are reported in this literature. Following the PRISMA-DTA method, we searched four databases for the period from January 2019 to December 2024. The screening was done by two independent reviewers. The main criterion was the 2 × 2 contingency table, reconstructed from sensitivity, specificity, and test set counts per class when it wasn’t reported directly. Pooled estimates were obtained using Reitsma’s bivariate random-effects model on the logit scale. Out of 329 identified records, 87 studies were included (Cohen’s kappa: 0.727). Seventeen of them, or 19.5%, reported the details needed to reconstruct a contingency table and were included in the meta-analysis. The pooled sensitivity was 0.983 (95% CI: 0.969 - 0.991) and the pooled specificity was 0.981 (95% CI: 0.968 - 0.988). The positive likelihood ratio reached 50.5 and the diagnostic odds ratio was 2922. There was high variability between studies, with the sensitivity prediction interval ranging from 0.833 to 0.999. The estimated correlation between sensitivity and specificity is positive (ρ = +0.868), indicating no threshold effect. The remaining 70 studies, or 80.5% of the set, reported only partly or not at all the sample sizes in the test set by class, so they were not pooled. Deep learning models show high performance for detecting brain tumors on MRI, but these estimates are based on a minority of studies and show significant heterogeneity. The main obstacle to synthesizing knowledge in this area isn’t the lack of studies but the incompleteness of their reporting: four out of five studies don’t properly publish the information needed to include their results in a quantitative analysis. Future publications should always report the full contingency table, or at least the sensitivity, specificity, and test set numbers by class.