ArticleFrontiers in oncology2022
MRI radiomic features of peritumoral edema may predict the recurrence sites of glioblastoma multiforme.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Epigenetic reprogramming and antitumor immune responses in gliomas: a systematic review.Medical oncology (Northwood, London, England) · 2025Pooled it
- Predicting early recurrence of craniopharyngioma using multi-omics radiomic modeling: a retrospective cohort study.Neurosurgical review · 2026Article
- Unique Peritumoral Patterns Unveiled by Quantitative Multiparametric MRI and Their Diagnostic and Prognostic Value in Treated Malignant Gliomas.NMR in biomedicine · 2026Article
- Article
- Magnetic susceptibility properties of tumor-associated cells imaged by MRI reveal glioblastoma infiltration in the edema region.Communications medicine · 2025Article
- Integrating Radiogenomics and Machine Learning in Musculoskeletal Oncology Care.Diagnostics (Basel, Switzerland) · 2025Review
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- Article
- Implication of tumor morphology and MRI characteristics on the accuracy of automated versus human segmentation of GBM areas.Scientific reports · 2025Article
- Individual Brain Tumor Invasion Mapping Based on Diffusion Kurtosis Imaging.Sovremennye tekhnologii v meditsine · 2025Article
- The Predictive Value of ADC Values, Degree of Edema and the Systemic Immune-Inflammation Index for Early Postoperative Recurrence in High-Grade Gliomas.Journal of inflammation research · 2025Article
- Clinicopathological Parameters and Immunohistochemical Profiles in Correlation with MRI Characteristics in Glioblastomas.International journal of molecular sciences · 2024Article
- Prognostic nomogram model based on quantitative metrics of subregions surrounding residual cavity in glioblastoma patients.Journal of cancer research and clinical oncology · 2024Article
- Radiomic Features as Artificial Intelligence Prognostic Models in Glioblastoma: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.Neuro-oncology · 2024Review
- Diagnosing Progression in Glioblastoma-Tackling a Neuro-Oncology Problem Using Artificial-Intelligence-Derived Volumetric Change over Time on Magnetic Resonance Imaging to Examine Progression-Free Survival in Glioblastoma.Diagnostics (Basel, Switzerland) · 2024Article
- Survival Outcome Prediction in Glioblastoma: Insights from MRI Radiomics.Current oncology (Toronto, Ont.) · 2024Article
- Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment.NPJ precision oncology · 2024Review
- The peritumoral brain zone in glioblastoma: a review of the pretreatment approach.Polish journal of radiology · 2024Review
- Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach.European journal of medical research · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and purpose: As one of the most aggressive malignant tumor in the central nervous system, the main cause of poor outcome of glioblastoma (GBM) is recurrence, a non-invasive method which can predict the area of recurrence pre-operation is necessary.To investigate whether there is radiological heterogeneity within peritumoral edema and identify the reproducible radiomic features predictive of the sites of recurrence of glioblastoma(GBM), which may be of value to optimize patients' management. Materials and methods: The clinical information and MR images (contrast-enhanced T1 weighted and FLAIR sequences) of 22 patients who have been histologically proven glioblastoma, were retrospectively evaluated. Kaplan-Meier methods was used for survival analysis. Oedematous regions were manually segmented by an expert into recurrence region, non-recurrence region. A set of 94 radiomic features were obtained from each region using the function of analyzing MR image of 3D slicer. Paired t test was performed to identify the features existing significant difference. Subsequently, the data of two patients from TCGA database was used to evaluate whether these features have clinical value. Results: Ten features with significant differences between the recurrence and non-recurrence subregions were identified and verified on two individual patients from the TCGA database with pathologically confirmed diagnosis of GBM. Conclusions: Our results suggested that heterogeneity does exist in peritumoral edema, indicating that the radiomic features of peritumoral edema from routine MR images can be utilized to predict the sites of GBM recurrence. Our findings may further guide the surgical treatment strategy for GBM.
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