ArticleNeuroradiology2020
Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.
Article in Neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- Noninvasive Determination ofAJNR. American journal of neuroradiology · 2021Pooled it
- A DTI-Radiomics and Clinical Integration Model for Predicting MCI-to-AD Progression Using Corpus Callosum Features.Current Alzheimer research · 2026Article
- Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.International journal of molecular sciences · 2025Review
- MRI-based radiomics model for differentiating focal cortical dysplasia from dysembryoplastic neuroepithelial tumor in epileptic children.Frontiers in neurology · 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
- 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
- Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.Quantitative imaging in medicine and surgery · 2023Article
- Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.Cancers · 2023Article
- The value of multiparametric MRI radiomics in predicting IDH genotype in glioma before surgery.Frontiers in oncology · 2023Article
- Deep-learning and conventional radiomics to predictFrontiers in oncology · 2023Article
- A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas.Journal of clinical medicine · 2022Review
- Mean apparent propagator-MRI in evaluation of glioma grade, cellular proliferation, and IDH-1 gene mutation status.European radiology · 2022Article
- Radiomics-based MRI for predicting Erythropoietin-producing hepatocellular receptor A2 expression and tumor grade in brain diffuse gliomas.Neuroradiology · 2022Article
- The progress of multimodal imaging combination and subregion based radiomics research of cancers.International journal of biological sciences · 2022Review
- Preoperative Diagnosis and Molecular Characterization of Gliomas With Liquid Biopsy and Radiogenomics.Frontiers in neurology · 2022Review
- Differentiating mass-like tuberculosis from lung cancer based on radiomics and CT features.Translational cancer research · 2021Article
- Reverse Engineering Glioma Radiomics to Conventional Neuroimaging.Neurologia medico-chirurgica · 2021Review
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Authors and funding
8 authors.
Funding
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Abstract
purposeTo evaluate whether diffusion tensor imaging (DTI) radiomics with machine learning improves the prediction of isocitrate dehydrogenase (IDH) mutation status of lower-grade gliomas beyond radiomic features from conventional MRI and DTI histogram parameters.
methodsA total of 168 patients with pathologically confirmed lower-grade gliomas were retrospectively enrolled. A total of 158 and 253 radiomic features were extracted from DTI (DTI radiomics) and conventional MRI (T1-weighted image with contrast enhancement, T2-weighted image, and FLAIR [conventional radiomics]), respectively. The random forest models for predicting IDH status were trained with variable combinations as follows: (1) DTI radiomics, (2) conventional radiomics, (3) conventional radiomics + DTI radiomics, and (4) conventional radiomics + DTI histogram. The models were validated with nested cross-validation. The predictive performances of those models were compared by using area under the curve (AUC) from receiver operating characteristic analysis, and 95% confidence interval (CI) was calculated.
resultsAdding DTI radiomics to conventional radiomics significantly improved the accuracy of IDH status subtyping (AUC, 0.900 [95% CI, 0.855-0.945], p = 0.006), whereas adding DTI histogram parameters yielded nonsignificant trend toward improvement (0.869 [95% CI, 0.816-0.922], p = 0.150) compared with the model with conventional radiomics alone (0.835 [95% CI, 0.773-0.896]). The performance of the model consisting of both DTI and conventional radiomics was significantly superior than that of model consisting of both DTI histogram parameters and conventional radiomics (0.900 vs 0.869, p = 0.040).
conclusionDTI radiomics with machine learning can help improve the subtyping of IDH status beyond conventional radiomics and DTI histogram parameters in patients with lower-grade gliomas.
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