ArticleNeuroImage. Clinical2018
Radiomics analysis allows for precise prediction of epilepsy in patients with low-grade gliomas.
Article in NeuroImage. Clinical, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Machine learning-based models for predicting glioma-associated epilepsy: a systematic review and meta-analysis.Discover oncology · 2025Article
- Leveraging pathological markers of lower grade glioma to predict the occurrence of secondary epilepsy, a retrospective study.Scientific reports · 2025Article
- Morphometric and radiomics analysis toward the prediction of epilepsy associated with supratentorial low-grade glioma in children.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Using partially shared radiomics features to simultaneously identify isocitrate dehydrogenase mutation status and epilepsy in glioma patients from MRI images.Scientific reports · 2025Article
- Radiomics features from the peritumoral region can be associated with the epilepsy status of glioblastoma patients.Frontiers in oncology · 2025Article
- Leveraging hand-crafted radiomics on multicenter FLAIR MRI for predicting disability worsening in people with multiple sclerosis.Frontiers in neuroscience · 2025Article
- Prognostic significance of collagen content in solitary fibrous tumors of the central nervous system.Frontiers in oncology · 2024Article
- T2-FLAIR imaging-based radiomic features for predicting early postoperative recurrence of grade II gliomas.Future oncology (London, England) · 2024Article
- Predicting Drug Treatment Outcomes in Childrens with Tuberous Sclerosis Complex-Related Epilepsy: A Clinical Radiomics Study.AJNR. American journal of neuroradiology · 2023Article
- Unruptured brain arteriovenous malformations causing seizures localize to one common brain network.Journal of neuroscience research · 2023Article
- Development and Validation of MRI-Based Radiomics Models for Diagnosing Juvenile Myoclonic Epilepsy.Korean journal of radiology · 2022Article
- Radiomic Analysis for Pretreatment Prediction of Recurrence Post-Radiotherapy in Cervical Squamous Cell Carcinoma Cancer.Diagnostics (Basel, Switzerland) · 2022Article
- Clinical evaluation of a novel atlas-based PET/CT brain image segmentation and quantification method for epilepsy.Quantitative imaging in medicine and surgery · 2022Article
- The persistence of seizures after tumor resection negatively affects survival in low-grade glioma patients: a clinical retrospective study.Journal of neurology · 2022Article
- Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas.Frontiers in oncology · 2022Article
- Altered Structural and Functional Patterns Within Executive Control Network Distinguish Frontal Glioma-Related Epilepsy.Frontiers in neuroscience · 2022Article
- MRI-Based Radiomics and Radiogenomics in the Management of Low-Grade Gliomas: Evaluating the Evidence for a Paradigm Shift.Journal of clinical medicine · 2021Review
- Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma.Frontiers in oncology · 2021Article
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12 authors.
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Abstract
Purpose: To investigate the association between imaging features and low-grade gliomas (LGG) related epilepsy, and to propose a radiomics-based model for the prediction of LGG-associated epilepsy. Methods: This retrospective study consecutively enrolled 286 patients with LGGs (194 in the primary cohort and 92 in the validation cohort). T2-weighted MR images (T2WI) were used to characterize risk factors for LGG-related epilepsy: Tumor location features and 3-D imaging features were determined, following which the interactions between these two kinds of features were analyzed. Elastic net was applied to generate a radiomics signature combining key imaging features associated with the LGG-related epilepsy with the primary cohort, and then a nomogram incorporating radiomics signature and clinical characteristics was developed. The radiomics signature and nomogram were validated in the validation cohort. Results: A total of 475 features associated with LGG-related epilepsy were obtained for each patient. A radiomics signature with eleven selected features allowed for discriminating patients with epilepsy or not was detected, which performed better than location and 3-D imaging features. The nomogram incorporating radiomics signature and clinical characteristics achieved a high degree of discrimination with area under receiver operating characteristic (ROC) curve (AUC) at 0.8769 in the primary cohort and 0.8152 in the validation cohort. The nomogram also allowed for good calibration in the primary cohort. Conclusion: We developed and validated an effective prediction model for LGG-related epilepsy. Our results suggested that radiomics analysis may enable more precise and individualized prediction of LGG-related epilepsy.
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