ArticleFrontiers in neuroscience2021
Radiomics Analysis Based on Magnetic Resonance Imaging for Preoperative Overall Survival Prediction in Isocitrate Dehydrogenase Wild-Type Glioblastoma.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- COVPRIG robustly predicts the overall survival of IDH wild-type glioblastoma and highlights METTL1Journal of translational medicine · 2023Pooled it
- From data silos to integrated diagnosis: multimodal machine learning in glioblastoma.Frontiers in oncology · 2026Review
- Machine learning-based integration of dosiomics and pre-radiotherapy multimodal MRI radiomics for survival stratification in patients with glioblastoma multiforme.Radiation oncology (London, England) · 2025Article
- Survival risk stratification of 2021 WHO glioblastoma by MRI radiomics and biological exploration.BMC cancer · 2025Article
- Robust radiomics: a review of guidelines for radiomics in medical imaging.Frontiers in radiology · 2025Review
- The prognostic value and biological significance of MRI CE-T1-based radiomics models in CNS5-identified GBM: a multi-center study.Scientific reports · 2024Article
- Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients.Journal of cancer research and clinical oncology · 2024Article
- Radiomics-Based Machine Learning with Natural Gradient Boosting for Continuous Survival Prediction in Glioblastoma.Cancers · 2024Article
- Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics.Current oncology (Toronto, Ont.) · 2024Article
- Radiomics in Spine Surgery.International journal of spine surgery · 2023Article
- Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.Quantitative imaging in medicine and surgery · 2023Article
- Predicting overall survival in diffuse glioma from the presurgical connectome.Scientific reports · 2022Article
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Authors and funding
11 authors.
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Abstract
purposeThis study aimed to develop a radiomics signature for the preoperative prognosis prediction of isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM) patients and to provide personalized assistance in the clinical decision-making for different patients. MATERIALS AND
methodsA total of 142 IDH-wild-type GBM patients classified using the new classification criteria of WHO 2021 from two centers were included in the study and randomly divided into a training set and a test set. Firstly, their clinical characteristics were screened using univariate Cox regression. Then, the radiomics features were extracted from the tumor and peritumoral edema areas on their contrast-enhanced T1-weighted image (CE-T1WI), T2-weighted image (T2WI), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) magnetic resonance imaging (MRI) images. Subsequently, inter- and intra-class correlation coefficient (ICC) analysis, Spearman's correlation analysis, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) Cox regression were used step by step for feature selection and the construction of a radiomics signature. The combined model was established by integrating the selected clinical factors. Kaplan-Meier analysis was performed for the validation of the discrimination ability of the model, and the C-index was used to evaluate consistency in the prediction. Finally, a Radiomics + Clinical nomogram was generated for personalized prognosis analysis and then validated using the calibration curve.
resultsAnalysis of the clinical characteristics resulted in the screening of four risk factors. The combination of ICC, Spearman's correlation, and univariate and LASSO Cox resulted in the selection of eight radiomics features, which made up the radiomics signature. Both the radiomics and combined models can significantly stratify high- and low-risk patients (
conclusionRadiomics is an independent preoperative non-invasive prognostic tool for patients who were newly classified as having IDH-wild-type GBM. The constructed nomogram, which combined radiomics features with clinical factors, can predict the overall survival (OS) of IDH-wild-type GBM patients and could be a new supplement to treatment guidelines.
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