ReviewCancers2019
A Review on a Deep Learning Perspective in Brain Cancer Classification.
Review in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers, 6 of them syntheses 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
121 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods.Journal of cancer research and clinical oncology · 2023Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Artificial Intelligence in Neurosurgery: a Systematic Review Using Topic Modeling. Part I: Major Research Areas.Sovremennye tekhnologii v meditsine · 2021Pooled it
- Current status and quality of radiomics studies in lymphoma: a systematic review.European radiology · 2020Pooled it
- Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis.Journal of medical systems · 2020Pooled it
- Saudi Patient-Derived Brain and Intracranial Explanted Tumor Cells: Isolation, Growth, and Anticancer Drug Screening.International journal of molecular sciences · 2026Article
- Multi-class classification of brain tumor using a ResNet101 backbone integrated with multi-scale deformable attention module and advanced data augmentations.Scientific reports · 2026Article
- Mesoporous silica nanoparticles in glioblastoma: smart nano-platforms for targeted therapy and precision diagnosis.3 Biotech · 2026Review
- Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.Frontiers in medicine · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Phosphoinositide-dependent kinase 1 (PDK1) in cancer: molecular insights and therapeutic strategies.Frontiers in chemistry · 2026Review
- Microwave head imaging systems for early brain tumor detection: antenna designs and emerging substrates.MethodsX · 2025Review
- Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis.Scientific reports · 2025Article
- The Clinical Role of miRNAs in the Development and Treatment of Glioblastoma.International journal of molecular sciences · 2025Review
- Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping.Scientific reports · 2025Article
- Gadolinium-Based Contrast Agents (GBCAs) for MRI: A Benefit-Risk Balance Analysis from a Chemical, Biomedical, and Environmental Point of View.Global challenges (Hoboken, NJ) · 2025Review
- Isoflavones: Promising Natural Agent for Cancer Prevention and Treatment.Food science & nutrition · 2025Review
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.Cancers · 2025Article
61 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer's, Parkinson's, and Wilson's disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.
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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.