ArticleNeuro-oncology2020
Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain.
Article in Neuro-oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 5 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
66 citing papers in PubMed, 5 syntheses or guidelines 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
- Machine learning radiomics for H3K27M mutation prediction in gliomas: A systematic review and meta-analysis.Neuroradiology · 2025Pooled it
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- Imaging features associated with H3 K27-altered and H3 G34-mutant gliomas: a narrative systematic review.Cancer imaging : the official publication of the International Cancer Imaging Society · 2022Pooled it
- Prediction of H3 K27M-mutant in midline gliomas by magnetic resonance imaging: a systematic review and meta-analysis.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
- Nanotechnology-based immunotherapy: integrating Artificial Intelligence (AI) with current strategies in combating brain cancer disease.Journal of the Egyptian National Cancer Institute · 2026Review
- Radiomics-guided multimodal MRI fusion framework for non-enhanced glioblastoma noninvasive identification.PLOS digital health · 2026Article
- Automated Machine Learning Frameworks for Radiomics: Comparative Evaluation Study.JMIR formative research · 2026Article
- Metabolic dependencies and neural progenitor dysregulation: driving forces in paediatric high-grade glioma development.Cancer metastasis reviews · 2026Review
- Integrating clinical features withBMC medical imaging · 2025Article
- Radiomics in pediatric brain tumors: from images to insights.Discover oncology · 2025Review
- The tree-based pipeline optimization tool: Tackling biomedical research problems with genetic programming and automated machine learning.Patterns (New York, N.Y.) · 2025Article
- Review
- Immunomodulatory role of vitamin D and emerging immunotherapies in hepatocellular carcinoma.Frontiers in nutrition · 2025Review
- Deep mutual learning on hybrid amino acid PET predicts H3K27M mutations in midline gliomas.NPJ precision oncology · 2024Article
- Review
- Article
- Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review.Cureus · 2024Review
- Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConventional MRI cannot be used to identify H3 K27M mutation status. This study aimed to investigate the feasibility of predicting H3 K27M mutation status by applying an automated machine learning (autoML) approach to the MR radiomics features of patients with midline gliomas.
methodsThis single-institution retrospective study included 100 patients with midline gliomas, including 40 patients with H3 K27M mutations and 60 wild-type patients. Radiomics features were extracted from fluid-attenuated inversion recovery images. Prior to autoML analysis, the dataset was randomly stratified into separate 75% training and 25% testing cohorts. The Tree-based Pipeline Optimization Tool (TPOT) was applied to optimize the machine learning pipeline and select important radiomics features. We compared the performance of 10 independent TPOT-generated models based on training and testing cohorts using the area under the curve (AUC) and average precision to obtain the final model. An independent cohort of 22 patients was used to validate the best model.
resultsTen prediction models were generated by TPOT, and the accuracy obtained with the best pipeline ranged from 0.788 to 0.867 for the training cohort and from 0.60 to 0.84 for the testing cohort. After comparison, the AUC value and average precision of the final model were 0.903 and 0.911 in the testing cohort, respectively. In the validation set, the AUC was 0.85, and the average precision was 0.855 for the best model.
conclusionsThe autoML classifier using radiomics features of conventional MR images provides high discriminatory accuracy in predicting the H3 K27M mutation status of midline glioma.
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.