ReviewNpj imaging2024
Applications of artificial intelligence in the analysis of histopathology images of gliomas: a review.
Review in Npj imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Stimulated Raman Spectroscopy for Intraoperative Glioblastoma Diagnosis-A Complementary Tool to Frozen Section?Cancers · 2026Article
- Multimodal fusion of pathology and radiology foundation models for WHO 2021 glioma subtyping.NPJ precision oncology · 2026Article
- AI-Based Methods in Neuropathology for Diagnosis and Treatment of Brain Tumors.European journal of neurology · 2026Review
- A structure-aware and causal-invariant framework for glioma cell classification in pathological images.Frontiers in cell and developmental biology · 2026Article
- Pediatric brain tumor classification using digital pathology and deep learning: Evaluation of SOTA methods on a multi-center Swedish cohort.Brain pathology (Zurich, Switzerland) · 2026Article
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Uncertainty-aware ensemble of foundation models differentiates glioblastoma from its mimics.Nature communications · 2025Article
- Artificial intelligence and systems biology analysis in stem cell research and therapeutics development.Stem cells translational medicine · 2025Review
- CRISPR and Artificial Intelligence in Neuroregeneration: Closed-Loop Strategies for Precision Medicine, Spinal Cord Repair, and Adaptive Neuro-Oncology.International journal of molecular sciences · 2025Review
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Patients With Glioblastoma.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Immune cell targeting-mediated cytomimetic drug delivery system for BBB-penetrating and precise therapy of in situ glioma.Materials today. Bio · 2025Article
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Pathology Foundation Models.JMA journal · 2025Review
- Multimodal Ensemble Fusion Deep Learning Using Histopathological Images and Clinical Data for Glioma Subtype Classification.IEEE access : practical innovations, open solutions · 2025Article
- SoloxoloneFrontiers in pharmacology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
In recent years, the diagnosis of gliomas has become increasingly complex. Analysis of glioma histopathology images using artificial intelligence (AI) offers new opportunities to support diagnosis and outcome prediction. To give an overview of the current state of research, this review examines 83 publicly available research studies that have proposed AI-based methods for whole-slide histopathology images of human gliomas, covering the diagnostic tasks of subtyping (23/83), grading (27/83), molecular marker prediction (20/83), and survival prediction (29/83). All studies were reviewed with regard to methodological aspects as well as clinical applicability. It was found that the focus of current research is the assessment of hematoxylin and eosin-stained tissue sections of adult-type diffuse gliomas. The majority of studies (52/83) are based on the publicly available glioblastoma and low-grade glioma datasets from The Cancer Genome Atlas (TCGA) and only a few studies employed other datasets in isolation (16/83) or in addition to the TCGA datasets (15/83). Current approaches mostly rely on convolutional neural networks (63/83) for analyzing tissue at 20x magnification (35/83). A new field of research is the integration of clinical data, omics data, or magnetic resonance imaging (29/83). So far, AI-based methods have achieved promising results, but are not yet used in real clinical settings. Future work should focus on the independent validation of methods on larger, multi-site datasets with high-quality and up-to-date clinical and molecular pathology annotations to demonstrate routine applicability.
Identifiers
What Socratic 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 Socratic graph.