ReviewNeuro-oncology2025
Applications of artificial intelligence and advanced imaging in pediatric diffuse midline glioma.
Review in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning in neuroimaging for predicting H3K27M mutations in diffuse midline gliomas: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Diffuse Midline Gliomas: Clinical, Diagnostic, and Therapeutic Perspectives.Biomedicines · 2026Review
- CTSB-positive tumor-associated macrophages shape prognosis and therapeutic response in lung adenocarcinoma.Translational oncology · 2026Article
- Prognostic role of delta radiomics in pediatric pontine diffuse midline gliomas.Journal of neuro-oncology · 2026Article
- Deep Learning Auto-segmentation of Diffuse Midline Glioma on Multimodal Magnetic Resonance Images.Journal of imaging informatics in medicine · 2026Article
- Voxel-based evaluation of hemorrhage risk in brain biopsies.Neurosurgical review · 2026Article
- A bibliometric analysis of progress and trends in pediatric glioma research.Discover oncology · 2026Article
- Research progress in imaging detection of brain metastases.Frontiers in medicine · 2026Review
- Review
- Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations.Clinical and experimental pediatrics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Diffuse midline glioma (DMG) is a rare, aggressive, and fatal tumor that largely occurs in the pediatric population. To improve outcomes, it is important to characterize DMGs, which can be performed via magnetic resonance imaging (MRI) assessment. Recently, artificial intelligence (AI) and advanced imaging have demonstrated their potential to improve the evaluation of various brain tumors, gleaning more information from imaging data than is possible without these methods. This narrative review compiles the existing literature on the intersection of MRI-based AI use and DMG tumors. The applications of AI in DMG revolve around classification and diagnosis, segmentation, radiogenomics, and prognosis/survival prediction. Currently published articles have utilized a wide spectrum of AI algorithms, from traditional machine learning and radiomics to neural networks. Challenges include the lack of cohorts of DMG patients with publicly available, multi-institutional, multimodal imaging and genomics datasets as well as the overall rarity of the disease. As an adjunct to AI, advanced MRI techniques, including diffusion-weighted imaging, perfusion-weighted imaging, and Magnetic Resonance Spectroscopy (MRS), as well as positron emission tomography (PET), provide additional insights into DMGs. Establishing AI models in conjunction with advanced imaging modalities has the potential to push clinical practice toward precision medicine.
Indexed as
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.