ArticleJournal of neuro-oncology2021
Radiomics-based prediction of multiple gene alteration incorporating mutual genetic information in glioblastoma and grade 4 astrocytoma, IDH-mutant.
Article in Journal of neuro-oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 5 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Radiomics Models as Tools for Predicting Genetic Mutations in Colorectal Cancer: A Systematic Review and Meta-Analysis.Journal of gastrointestinal cancer · 2026Pooled it
- Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis.European radiology · 2024Pooled 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
- Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis.Frontiers in radiology · 2024Pooled it
- Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- Review
- Explainable incremental-value analysis of apparent diffusion coefficient and arterial spin labeling radiomics for ATRX status prediction in glioblastoma.Frontiers in oncology · 2026Article
- Identification of intrinsic imaging subtypes using clustering analysis based on dynamic contrast-enhanced magnetic resonance imaging radiomics features for gliomas: preliminary associations with gene expression profiles.Quantitative imaging in medicine and surgery · 2025Article
- Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 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
- -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
- Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences.Journal of radiation research · 2024Article
- Computed tomography radiogenomics: A potential tool for prediction of molecular subtypes in gastric stromal tumor.World journal of gastrointestinal oncology · 2024Article
- Newly Recognized Genetic Tumor Syndromes of the CNS in the 5th WHO Classification: Imaging Overview with Genetic Updates.AJNR. American journal of neuroradiology · 2024Review
- A subregion-based RadioFusionOmics model discriminates between grade 4 astrocytoma and glioblastoma on multisequence MRI.Journal of cancer research and clinical oncology · 2024Article
- Case report: A 53-year-old woman with synchronous WHO classification II and IV gliomas.Frontiers in oncology · 2024Article
- Development of A Radiomic Model forInternational journal of molecular sciences · 2023Article
- Dynamic contrast-enhanced MRI radiomics model predicts epidermal growth factor receptor amplification in glioblastoma, IDH-wildtype.Journal of neuro-oncology · 2023Article
- Novel insight into histological and molecular astrocytoma, IDH-mutant, Grade 4 by the updated WHO classification of central nervous system tumors.Cancer medicine · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
purposeIn glioma, molecular alterations are closely associated with disease prognosis. This study aimed to develop a radiomics-based multiple gene prediction model incorporating mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant.
methodsFrom December 2014 through January 2020, we enrolled 418 patients with pathologically confirmed glioblastoma (based on the 2016 WHO classification). All selected patients had preoperative MRI and isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor amplification, and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) loss status. Patients were randomly split into training and test sets (7:3 ratio). Enhancing tumor and peritumoral T2-hyperintensity were auto-segmented, and 660 radiomics features were extracted. We built binary relevance (BR) and ensemble classifier chain (ECC) models for multi-label classification and compared their performance. In the classifier chain, we calculated the mean absolute Shapley value of input features.
resultsThe micro-averaged area under the curves (AUCs) for the test set were 0.804 and 0.842 in BR and ECC models, respectively. IDH mutation status was predicted with the highest AUCs of 0.964 (BR) and 0.967 (ECC). The ECC model showed higher AUCs than the BR model for ATRX (0.822 vs. 0.775) and MGMT promoter methylation (0.761 vs. 0.653) predictions. The mean absolute Shapley values suggested that predicted outcomes from the prior classifiers were important for better subsequent predictions along the classifier chains.
conclusionWe built a radiomics-based multiple gene prediction chained model that incorporates mutual information of each genetic alteration in glioblastoma and grade 4 astrocytoma, IDH-mutant and performs better than a simple bundle of binary classifiers using prior classifiers' prediction probability.
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