ArticleKorean journal of radiology2019
Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.
Article in Korean journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis.Frontiers in oncology · 2022Pooled it
- A single MRI scan contains sufficient imaging information for accurate prediction of meningioma growth risk.NeuroImage. Clinical · 2026Article
- Computed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study.Cancers · 2025Article
- Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Completely non-invasive prediction of IDH mutation status based on preoperative native CT images.Scientific reports · 2024Article
- Impact Exploration of Spatiotemporal Feature Derivation and Selection on Machine Learning-Based Predictive Models for Post-Embolization Cerebral Aneurysm Recanalization.Cardiovascular engineering and technology · 2024Article
- Radiomic feature reliability of amide proton transfer-weighted MR images acquired with compressed sensing at 3T.International journal of imaging systems and technology · 2024Article
- Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification.European radiology · 2024Article
- Article
- Multiomics Approach to Acromegaly: Unveiling Translational Insights for Precision Medicine.Endocrinology and metabolism (Seoul, Korea) · 2023Review
- Use Test of Automated Machine Learning in Cancer Diagnostics.Diagnostics (Basel, Switzerland) · 2023Article
- Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain.Journal of personalized medicine · 2023Article
- The combination of radiomics features and VASARI standard to predict glioma grade.Frontiers in oncology · 2023Article
- Radiomics analysis of amide proton transfer-weighted and structural MR images for treatment response assessment in malignant gliomas.NMR in biomedicine · 2023Article
- Quality reporting of radiomics analysis in pituitary adenomas: promoting clinical translation.The British journal of radiology · 2022Article
- A Survey of Radiomics in Precision Diagnosis and Treatment of Adult Gliomas.Journal of clinical medicine · 2022Review
- Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion.Cancers · 2022Article
- Machine learning for emerging infectious disease field responses.Scientific reports · 2022Article
- Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.NPJ precision oncology · 2021Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. MATERIALS AND
methodsTwo-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade.
resultsThe performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68).
conclusionRadiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.
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