ReviewRadiology2019
Emerging Applications of Artificial Intelligence in Neuro-Oncology.
Review in Radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 3 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
119 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Automated longitudinal treatment response assessment of brain tumors: A systematic review.Neuro-oncology · 2025Pooled it
- Current state of radiomics in pediatric neuro-oncology practice: a systematic review.Pediatric radiology · 2023Pooled it
- Application of artificial intelligence and radiomics in pituitary neuroendocrine and sellar tumors: a quantitative and qualitative synthesis.Neuroradiology · 2022Pooled it
- Automated Determination of the H3 K27-Altered Status in Spinal Cord Diffuse Midline Glioma by Radiomics Based on T2-Weighted MR Images.AJNR. American journal of neuroradiology · 2023Trial
- Leveraging traditional and advanced machine learning for comprehensive oncology data integration and outcome prediction.Journal of neuro-oncology · 2026Review
- Beyond the mutation: integrating radiogenomics, epigenetics, and immune signatures to overcome therapeutic resistance in CNS tumors: a narrative review.Annals of medicine and surgery (2012) · 2026Article
- Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma.NPJ precision oncology · 2026Article
- Neurosurgical Application of Artificial Intelligence in Pediatric Neuro-Oncology.Journal of Korean Neurosurgical Society · 2026Article
- LILRB3 inhibition reverses immunosuppression in glioma: a nanoparticle-based therapeutic strategy.Journal of nanobiotechnology · 2026Article
- Decoding the Glioblastoma Microenvironment: AI-Driven Analysis of Cellular MRI Signatures for Targeted Therapy.Cellular and molecular neurobiology · 2026Review
- Development of the glioma imaging complexity score (GICS): a volumetric MRI-based stratification framework.Frontiers in surgery · 2026Article
- Characterizing industry payments for FDA-approved AI medical devices.Health affairs scholar · 2025Article
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Emerging Trends in Artificial Intelligence in Neuro-Oncology.Current oncology reports · 2025Review
- Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.Neuro-oncology · 2025Review
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- Toward diffusion MRI in the diagnosis and treatment of pancreatic cancer.Medical oncology (Northwood, London, England) · 2025Review
- Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.AJNR. American journal of neuroradiology · 2025Article
- A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification.NPJ digital medicine · 2025Article
59 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
5 authors.
Funding
Abstract
Due to the exponential growth of computational algorithms, artificial intelligence (AI) methods are poised to improve the precision of diagnostic and therapeutic methods in medicine. The field of radiomics in neuro-oncology has been and will likely continue to be at the forefront of this revolution. A variety of AI methods applied to conventional and advanced neuro-oncology MRI data can already delineate infiltrating margins of diffuse gliomas, differentiate pseudoprogression from true progression, and predict recurrence and survival better than methods used in daily clinical practice. Radiogenomics will also advance our understanding of cancer biology, allowing noninvasive sampling of the molecular environment with high spatial resolution and providing a systems-level understanding of underlying heterogeneous cellular and molecular processes. By providing in vivo markers of spatial and molecular heterogeneity, these AI-based radiomic and radiogenomic tools have the potential to stratify patients into more precise initial diagnostic and therapeutic pathways and enable better dynamic treatment monitoring in this era of personalized medicine. Although substantial challenges remain, radiologic practice is set to change considerably as AI technology is further developed and validated for clinical use.
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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.