ArticleJournal of cancer research and clinical oncology2019
Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.
Article in Journal of cancer research and clinical oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 4 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled 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
- Pooled it
- Radiomics features from the peritumoral region can be associated with the epilepsy status of glioblastoma patients.Frontiers in oncology · 2025Article
- -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
- Two-Stage Training Framework Using Multicontrast MRI Radiomics forRadiology. Artificial intelligence · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- [A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- An Explainable Radiogenomic Framework to Predict Mutational Status of KRAS and EGFR in Lung Adenocarcinoma Patients.Bioengineering (Basel, Switzerland) · 2023Article
- Predicting isocitrate dehydrogenase genotype, histological phenotype, and Ki-67 expression level in diffuse gliomas with an advanced contrast analysis of magnetic resonance imaging sequences.Quantitative imaging in medicine and surgery · 2023Article
- MRI radiomics and potential applications to glioblastoma.Frontiers in oncology · 2023Review
- Investigating the value of radiomics stemming from DSC quantitative biomarkers in IDH mutation prediction in gliomas.Frontiers in neurology · 2023Article
- Deep-learning and conventional radiomics to predictFrontiers in oncology · 2023Article
- Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning.Diagnostics (Basel, Switzerland) · 2022Article
- Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas.Journal of clinical medicine · 2022Review
- RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas.Cancers · 2022Article
- Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion.Cancers · 2022Article
- Review
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5 authors.
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Abstract
purposeReliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.
methodsEight classical machine learning methods were evaluated in terms of their stability and performance for pre-operative IDH genotype prediction. A total of 126 patients were enrolled for analysis. Overall, 704 radiomic features extracted from the pre-operative MRI images were analyzed. The patients were randomly assigned to either the training set or the validation set at a ratio of 2:1. Feature selection and classification model training were done using the training set, whereas the predictive performance and stability of the model were independently assessed using the validation set.
resultsRandom Forest (RF) showed high predictive performance (accuracy 0.885 ± 0.041, AUC 0.931 ± 0.036), whereas neural network (NN) (accuracy 0.829 ± 0.064, AUC 0.878 ± 0.052) and flexible discriminant analysis (FDA) (accuracy 0.851 ± 0.049, AUC 0.875 ± 0.057) displayed low predictive performance. With regard to stability, RF also showed high robustness against data perturbation (relative standard deviations, RSD 3.87%).
conclusionsRF is a promising machine learning method in predicting IDH genotype. Development of an accurate and reliable model can assist in the initial diagnostic evaluation and treatment planning for diffuse glioma patients.
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