Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI.

Overview

1. Ailice Labs, Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, 27100 Pavia, Italy; (M.A.); (C.C.); (C.S.)
2. Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Institute of Bari, 70124 Bari, Italy (G.C.S.); (G.L.)
3. Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Unit of Bari Institute, 70124 Bari, Italy
4. Department of Physical and Rehabilitation Medicine, University of Foggia, 71122 Foggia, Italy
5. Spasticity and Movement Disorders “ReSTaRt”, Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
6. Department of Biomedical Sciences for Health, Università degli Studi di Milano, 20122 Milan, Italy
7. Unit of Radiology, IRCCS Galeazzi-Sant’Ambrogio Hospital, 20157 Milan, Italy
8. Department of Physics ‘‘Giuseppe Occhialini”, University of Milan-Bicocca, 20126 Milan, Italy
9. CDI Centro Diagnostico Italiano, 20147 Milan, Italy
10. Department of Diagnostic Imaging and Stereotactic Radiosurgery, Centro Diagnostico Italiano S.p.A., 20147 Milan, Italy
11. Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), University of Salerno, 84084 Fisciano, Italy
12. DeepTrace Technologies S.R.L., 20122 Milan, Italy

Abstract

Highlights: What are the main findings? In total, 31 of 310 studies (2022–2025) met the inclusion criteria; eight did both segmentation and classification. CNN-based (especially U-Net variants) and hybrid models dominate glioma segmentation and classification, but clinical translation remains limited due to a lack of external validation, dataset bias, and heterogeneous methodologies.

What are the implications of the main findings? Future research should prioritize cross-institutional validation on independent clinical cohorts and the adoption of standardized evaluation and reporting protocols to improve reliability and comparability. Integrating imaging with clinical and molecular data, together with the systematic use of explainability methods, will be essential to develop robust and clinically deployable AI models.

Abstract: Background/Objectives: Brain tumors are highly lethal cancers, with gliomas representing the most complex subtype. Magnetic resonance imaging (MRI) is the main non-invasive imaging modality. This review evaluates deep learning (DL) and artificial intelligence methods for brain tumor segmentation and classification. Methods: In this systematic review, PubMed and Scopus were searched for articles published from 2022 to March 2025. Authors independently identified eligible studies based on predefined inclusion criteria and extracted data. The study quality and risk of bias were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist. Results: Thirty-one studies met the inclusion criteria from 310 records, with eight addressing both segmentation and classification. Most segmentation studies used publicly available multiparametric MRI datasets. Performance varied by architecture and tumor region, with whole-tumor segmentation achieving the highest Dice Similarity Coefficient (DSC). Classical U-Nets reported DSC values ranging 80–87%, while models with residual or attention mechanisms exceeded 90%. Classification focused on tumor type and glioma grading, using features learned from multiparametric MRI. Reported accuracy ranged from 91.3% to 99.4%, with sensitivity and specificity often above 95%. However, variability across tumor subregions, limited external validation, reliance on public datasets, and heterogeneous preprocessing raise concerns about robustness and real-world generalizability. Evidence on the use of explainability methods for both tasks remains limited. Conclusions: DL models for glioma segmentation and classification demonstrate promising performance. However, standardized validation protocols, multi-center datasets, and the integration of explainable artificial intelligence techniques are needed to improve transparency, robustness, and clinical applicability.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• figshare:1512427, at figshare; found in the references
• kaggle.com/datasets/masoudnickparvar, at Kaggle; found in the references
• kaggle.com/datasets/sartajbhuvaji, at Kaggle; found in the references

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 14 authors, 6 keywords, 88 references.

Cite

This paper

Aresta, S., Palmirotta, C., Asim, M., Battista, P., Santi, G. C., Lagravinese, G., Cava, C., Fiore, P., Santamato, A., Vitali, P., Castiglioni, I., D’Anna, G., Rundo, L., & Salvatore, C. (2026). Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI. Brain sciences, 16(5), 468. https://doi.org/10.3390/brainsci16050468

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