ArticleJournal of imaging2021
The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-driven estimation of O6 methylguanine-DNA-methyltransferase (MGMT) promoter methylation in glioblastoma patients: a systematic review with bias analysis.Journal of cancer research and clinical oncology · 2024Pooled it
- Imaging Biomarkers in Radiotherapy.Cancers · 2026Review
- Timepoint-Specific Benchmarking of Deep Learning Models for Glioblastoma Follow-Up MRI.Cancers · 2025Article
- Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma.European journal of medical research · 2025Article
- Radiomics and Radiogenomics in Differentiating Progression, Pseudoprogression, and Radiation Necrosis in Gliomas.Biomedicines · 2025Review
- Clinical outcome and deep learning imaging characteristics of patients treated by radio-chemotherapy for a "molecular" glioblastoma.The oncologist · 2025Article
- Prediction of Rapid Early Progression and Survival Risk with Pre-Radiation MRI in WHO Grade 4 Glioma Patients.Cancers · 2023Article
- One Step Forward-The Current Role of Artificial Intelligence in Glioblastoma Imaging.Life (Basel, Switzerland) · 2023Review
- Article
- Relapsing High-Grade Glioma from Peritumoral Zone: Critical Review of Radiotherapy Treatment Options.Brain sciences · 2022Review
- Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review.Biomedicines · 2022Review
- Radiomics for precision medicine in glioblastoma.Journal of neuro-oncology · 2022Review
- Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and ONeuro-oncology advancesArticle
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) promoter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clinical features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre-RT MRIs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.