ArticleScientific reports2018
Lesion location implemented magnetic resonance imaging radiomics for predicting IDH and TERT promoter mutations in grade II/III gliomas.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 3 of them syntheses that pooled it.
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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
71 citing papers in PubMed, 3 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- Telomerase activity, TERT expression, hTERT promoter alterations, and alternative lengthening of the telomeres (ALT) in meningiomas - a systematic review.Neurosurgical review · 2020Pooled it
- Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- Image-based Re-evaluation of the JCOG0911 Study Focusing on Tumor Volume and Survival, Disease Progression Diagnosis, and Radiomic Prognostication for Newly Diagnosed Glioblastoma.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Trial
- Association of TERT promoter mutation and MGMT methylation with MRI features and overall survival in glioblastoma, IDH-wildtype.Quantitative imaging in medicine and surgery · 2026Article
- Radiomics-based models to predict IDH mutation status and prognosis in gliomas using MRI: a multicenter study.Frontiers in oncology · 2026Article
- Integrating deep learning and radiomics for preoperative glioma grading using multi-center MRI data.Scientific reports · 2025Article
- MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas.NPJ precision oncology · 2025Article
- 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
- Article
- [Research Progress in Imaging Investigation of TERT Promoter Mutations in Gliomas].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2024Review
- -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2-4 glioma patients: a deep Radiomics Approach.BMC medical imaging · 2024Article
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- Relationship between glioblastoma location and OBrain communications · 2024Article
- Phenomic Imaging.Phenomics (Cham, Switzerland) · 2023Review
- Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.Journal of neuro-oncology · 2023Article
- Article
- Assessment of brain cancer atlas maps with multimodal imaging features.Journal of translational medicine · 2023Review
- Zinc Finger MYND-Type Containing 8 (ZMYND8) Is Epigenetically Regulated in Mutant Isocitrate Dehydrogenase 1 (IDH1) Glioma to Promote Radioresistance.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
24 authors at 14 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular biological characterization of tumors has become a pivotal procedure for glioma patient care. The aim of this study is to build conventional MRI-based radiomics model to predict genetic alterations within grade II/III gliomas attempting to implement lesion location information in the model to improve diagnostic accuracy. One-hundred and ninety-nine grade II/III gliomas patients were enrolled. Three molecular subtypes were identified: IDH1/2-mutant, IDH1/2-mutant with TERT promoter mutation, and IDH-wild type. A total of 109 radiomics features from 169 MRI datasets and location information from 199 datasets were extracted. Prediction modeling for genetic alteration was trained via LASSO regression for 111 datasets and validated by the remaining 58 datasets. IDH mutation was detected with an accuracy of 0.82 for the training set and 0.83 for the validation set without lesion location information. Diagnostic accuracy improved to 0.85 for the training set and 0.87 for the validation set when lesion location information was implemented. Diagnostic accuracy for predicting 3 molecular subtypes of grade II/III gliomas was 0.74 for the training set and 0.56 for the validation set with lesion location information implemented. Conventional MRI-based radiomics is one of the most promising strategies that may lead to a non-invasive diagnostic technique for molecular characterization of grade II/III gliomas.
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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.