ArticleJournal of personalized medicine2020
XGBoost Improves Classification of MGMT Promoter Methylation Status in IDH1 Wildtype Glioblastoma.
Article in Journal of personalized medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
52 citing papers in PubMed, 3 syntheses or guidelines pooled it, 112 citations in OpenAlex.
- Diagnostic Accuracy of Artificial Intelligence for Predicting MGMT Promoter Methylation in Glioblastoma Using MR Imaging: A Systematic Review.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Pooled it
- AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2021Pooled it
- Label-Free Raman Spectroscopy Reveals Metabolic Signatures Associated with MGMT Promoter Methylation Status in Glioblastoma.Analytical chemistry · 2026Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI.Diagnostics (Basel, Switzerland) · 2025Review
- Predicting podoplanin expression and prognostic significance in high-grade glioma based on TCGA TCIA radiomics.PloS one · 2025Article
- Leveraging machine learning to enhance appointment adherence at a novel post-discharge care transition clinic.JAMIA open · 2024Article
- Adaptive fine-tuning based transfer learning for the identification of MGMT promoter methylation status.Biomedical physics & engineering express · 2024Article
- Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events.NPJ systems biology and applications · 2024Article
- Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.Journal of radiation research · 2024Article
- Article
- Revisiting Temozolomide's role in solid tumors: Old is gold?Journal of Cancer · 2024Review
- Multi-modal deep learning from imaging genomic data for schizophrenia classification.Frontiers in psychiatry · 2024Article
- Development of A Radiomic Model forInternational journal of molecular sciences · 2023Article
- Prognostic Value of Immunohistochemical Expression of MTAP and AKIP1 in IDH1 Mutant Astrocytoma.Asian Pacific journal of cancer prevention : APJCP · 2023Article
- Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.Quantitative imaging in medicine and surgery · 2023Article
- MRI-Based Deep Learning Tools for MGMT Promoter Methylation Detection: A Thorough Evaluation.Cancers · 2023Article
- Insight into the Progress in CAR-T Cell Therapy and Combination with Other Therapies for Glioblastoma.International journal of general medicine · 2023Review
Corrections and comments
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Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
Abstract
Approximately 96% of patients with glioblastomas (GBM) have IDH1 wildtype GBMs, characterized by extremely poor prognosis, partly due to resistance to standard temozolomide treatment. O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation status is a crucial prognostic biomarker for alkylating chemotherapy resistance in patients with GBM. However, MGMT methylation status identification methods, where the tumor tissue is often undersampled, are time consuming and expensive. Currently, presurgical noninvasive imaging methods are used to identify biomarkers to predict MGMT methylation status. We evaluated a novel radiomics-based eXtreme Gradient Boosting (XGBoost) model to identify MGMT promoter methylation status in patients with IDH1 wildtype GBM. This retrospective study enrolled 53 patients with pathologically proven GBM and tested MGMT methylation and IDH1 status. Radiomics features were extracted from multimodality MRI and tested by F-score analysis to identify important features to improve our model. We identified nine radiomics features that reached an area under the curve of 0.896, which outperformed other classifiers reported previously. These features could be important biomarkers for identifying MGMT methylation status in IDH1 wildtype GBM. The combination of radiomics feature extraction and F-core feature selection significantly improved the performance of the XGBoost model, which may have implications for patient stratification and therapeutic strategy in GBM.
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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.