ArticleScientific reports2020
Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Identification of exosomal miRNA-based predictive signatures for gestational diabetes mellitus via multi-algorithm machine learning.BMC pregnancy and childbirth · 2026Article
- Radiomics-Driven Hybrid Deep Learning for MRI-Based Prediction of Glioma Grade and 1p/19q Codeletion.Tomography (Ann Arbor, Mich.) · 2026Article
- Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status: Where Are We?Medicina (Kaunas, Lithuania) · 2025Review
- Deep learning classification of MGMT status of glioblastomas using multiparametric MRI with a novel domain knowledge augmented mask fusion approach.Scientific reports · 2025Article
- The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Recent patents on anti-cancer drug discovery · 2025Review
- A data-centric machine learning approach to improve prediction of glioma grades using low-imbalance TCGA data.Scientific reports · 2024Article
- Brain tumour genetic network signatures of survival.Brain : a journal of neurology · 2023Article
- Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.Cancers · 2023Article
- Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.Current oncology (Toronto, Ont.) · 2023Review
- AutoComBat: a generic method for harmonizing MRI-based radiomic features.Scientific reports · 2022Article
- Predicting MGMT Promoter Methylation in Diffuse Gliomas Using Deep Learning with Radiomics.Journal of clinical medicine · 2022Article
- Convolutional neural networks to predict brain tumor grades and Alzheimer's disease with MR spectroscopic imaging data.PloS one · 2022Article
- Early Surgery Prolongs Professional Activity in IDH Mutant Low-Grade Glioma Patients: A Policy Change Analysis.Frontiers in oncology · 2022Article
- Artificial Intelligence for Precision Oncology.Advances in experimental medicine and biology · 2022Review
- Pseudo-insular glioma syndrome: illustrative cases.Journal of neurosurgery. Case lessons · 2021Article
- Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine.Cancer communications (London, England) · 2021Review
- Article
- Review
- Artificial Intelligence in Cancer Research and Precision Medicine.Cancer discovery · 2021Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We introduce molecular (MGMT methylation, IDH mutation, 1p/19q co-deletion, ATRX mutation, and TERT mutations) prediction methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance imaging data and include texture features, fractal and multi-resolution fractal texture features, and volumetric features. Training models include nested leave-one-out cross-validation to select features, train the model, and estimate model performance. The prediction models of MGMT methylation, IDH mutations, 1p/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of 0.83 ± 0.04, 0.84 ± 0.03, 0.80 ± 0.04, 0.70 ± 0.09, and 0.82 ± 0.04, respectively. Furthermore, our analysis shows that the fractal features have a significant effect on the predictive performance of MGMT methylation IDH mutations, 1p/19q co-deletion, and ATRX mutations. The performance of our prediction methods indicates the potential of correlating computed imaging features with LGG molecular mutations types and identifies candidates that may be considered potential predictive biomarkers of LGG molecular classification.
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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.