Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.
Authors
Affiliations (2)
Affiliations (2)
- Department of Internal Medicine, College of Medicine, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Abstract
<b>Background</b>: Magnetic resonance imaging (MRI) is central to brain tumor segmentation and histological grading, and deep learning (DL) has transformed both tasks. Existing reviews rarely span the 2017-2026 architectural arc from CNNs and U-Net variants to transformers and foundation models or appraise reproducibility and clinical-translation readiness. <b>Methods</b>: This preregistered systematic review (PRISMA 2020, PRISMA-S) searched Google Scholar, PubMed/MEDLINE, and IEEE Xplore on 21 May 2026 (January 2017-May 2026). A single reviewer performed screening, extraction and QUADAS-AI appraisal with repeated checks on separate days; therefore, the synthesis is presented as a transparent descriptive review rather than a pooled meta-analysis. Records were screened against a priori eligibility criteria; primary experimental studies entered the synthesis and review articles formed a contextual corpus. Methodological quality was appraised using an adapted QUADAS framework ("QUADAS-AI"). Heterogeneity precluded statistical pooling, so a benchmark-driven comparative synthesis was conducted. <b>Results</b>: The search retrieved 33,982 records (Google Scholar 23,400; PubMed/MEDLINE 5349; IEEE Xplore 5233). After deduplication and screening, 141 full texts were assessed; one report published before the eligibility window was excluded, leaving 140 included studies: 117 primary (39 contributed to the BraTS Dice benchmark sub-set) and 23 contextual reviews. U-Net variants (42%) and hybrid CNN-transformer architectures (40%) dominate, followed by CNN classifiers (14%) and vision transformers (3%). On BraTS 2021, nnU-Net and Swin UNETR reach DSC 0.93/0.90/0.85 (whole tumor/core/enhancing); reported external evaluations show 2.5-15 percentage-point performance drops under domain shift. <b>Conclusions</b>: External generalizability, uncertainty quantification, reproducibility, and prospective validation remain weak; ten research priorities are proposed. <b>Registration</b>: OSF, DOI 10.17605/OSF.IO/C2QA6 (https://osf.io/c2qa6); registered 20 May 2026.