ReviewNPJ precision oncology2025
A review of deep learning for brain tumor analysis in MRI.
Review in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Metabolic Imaging as Future Technology and Innovation in Brain-Tumour Surgery: A Systematic Review.Current oncology (Toronto, Ont.) · 2025Pooled it
- CT-based deep learning radiogenomics for predicting key glioma genotypes (IDH, ATRX, EGFR, TP53).Neuroradiology · 2026Article
- Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study.The Lancet medical imaging & theranostics · 2026Article
- Brain Tumor Segmentation and Grading on MRI Using Deep Learning: A Systematic Literature Review and Benchmark-Driven Comparative Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- [Application of hybrid attention guided dual-path residual learning in magnetic resonance imaging diagnosis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- CAE-BrainNet: a statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI.Brain informatics · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- GDNet: A Robust 2.5D Multimodal MRI Brain Tumor Segmentation Framework with EMA Stabilization and Tumor-Aware Sampling.Journal of imaging · 2026Article
- An Explainable Multi-Scale Deep Learning Framework for Multi-Class Brain MRI Classification.Diagnostics (Basel, Switzerland) · 2026Article
- 3D Deep Learning for Brain Tumor Segmentation and Survival Prediction: A Comprehensive Multi-Modal Analysis Using the BraTS2020 Dataset.Journal of imaging · 2026Article
- A Dual-Branch Frequency-Aware Attention Framework for Rare Neurological Disease Classification from Brain MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Exploring the role of advanced MRI in understanding glioblastoma biology: A scoping review protocol.MethodsX · 2026Article
- BRAIN-META: A reproducible CNN-vision transformer meta-ensemble pipeline for explainable brain tumor classification.MethodsX · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- PF-CMNet: Progressive Frequency-Aware Cross-Modal Network with Missing-Modality Distillation for 3D Brain Tumor Segmentation.Brain sciences · 2026Article
- Brain Cancer: Molecular Alterations and Emerging Trends in Neuropharmacology.International journal of molecular sciences · 2026Review
- Quantum-inspired optimization of transformer-capsule networks for accurate brain tumor segmentation and classification.Scientific reports · 2026Article
- YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.Scientific reports · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- Multi-Chaotic HEOA for Hardware-Aware Neural Architecture Search: Brain Tumor Classification on FPGA.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Recent progress in deep learning (DL) is producing a new generation of tools across numerous clinical applications. Within the analysis of brain tumors in magnetic resonance imaging, DL finds applications in tumor segmentation, quantification, and classification. It facilitates objective and reproducible measurements crucial for diagnosis, treatment planning, and disease monitoring. Furthermore, it holds the potential to pave the way for personalized medicine through the prediction of tumor type, grade, genetic mutations, and patient survival outcomes. In this review, we explore the transformative potential of DL for brain tumor care and discuss existing applications, limitations, and future directions and opportunities.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.