ArticleRadiation oncology (London, England)2021
FET-PET radiomics in recurrent glioblastoma: prognostic value for outcome after re-irradiation?
Article in Radiation oncology (London, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [European journal of nuclear medicine and molecular imaging · 2021Trial
- PET|MR Open Source Radiomics Models for Treatment Outcome Prediction in Recurrent GBM.Cancers · 2026Article
- PET-based Radiomics Analysis for Predicting Prognosis and Differentiation Treatment-Related Changes in Glioma: A Systematic Review.Journal of biomedical physics & engineering · 2026Article
- A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma.Neuro-oncology · 2026Article
- Biological tumor volume predicts survival in recurrent High-Grade glioma: A multiparametric [European journal of nuclear medicine and molecular imaging · 2026Article
- Multimodal Imaging to Improve Patient Selection and Optimize Treatment Planning and Delivery for Patients Undergoing Reirradiation: A Scoping Review.Current oncology reports · 2025Article
- Construction of a prognostic survival model for colorectal cancer patients using CT image texture analysis: a prospective cohort study.Frontiers in oncology · 2025Article
- Contribution of [Pharmaceuticals (Basel, Switzerland) · 2024Review
- Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [Scientific reports · 2024Article
- Application of PET imaging delta radiomics for predicting progression-free survival in rare high-grade glioma.Scientific reports · 2024Article
- Advances in the field of developing biomarkers for re-irradiation: a how-to guide to small, powerful data sets and artificial intelligence.Expert review of precision medicine and drug development · 2024Article
- Review
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
- Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics.La Radiologia medica · 2023Article
- Highlighted Advances in Therapies for Difficult-To-Treat Brain Tumours Such as Glioblastoma.Pharmaceutics · 2023Review
- Amino Acid Tracer PET MRI in Glioma Management: What a Neuroradiologist Needs to Know.AJNR. American journal of neuroradiology · 2023Review
- Research on application of radiomics in glioma: a bibliometric and visual analysis.Frontiers in oncology · 2023Review
- Combination of pre-treatment dynamic [European journal of nuclear medicine and molecular imaging · 2023Article
- Radiomics in Determining Tumor-to-Normal Brain SUV Ratio Based onSovremennye tekhnologii v meditsine · 2023Article
- Article
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Abstract
purposeThe value of O-(2-[18F]fluoroethyl)-L-tyrosine (FET)-positron emission tomography (PET)-radiomics in the outcome assessment of patients with recurrent glioblastoma (rGBM) has not been evaluated until now. The aim of this study was to evaluate whether a prognostic model based on FET-PET radiomics features (RF) is feasible and can identify rGBM patients that would most benefit from re-irradiation.
methodsWe prospectively recruited rGBM patients who underwent FET-PET before re-irradiation (GLIAA-Pilot trial, DRKS00000633). Tumor volume was delineated using a semi-automatic method with a threshold of 1.8 times the standardized-uptake-value of the background. 135 FET-RF (histogram parameters, shape and texture features) were extracted. The analysis involved the characterization of tumor and non-tumor tissue with FET-RF and the evaluation of the prognostic value of FET-RF for time-to-progression (TTP), overall survival (OS) and recurrence location (RL).
resultsThirty-two rGBM patients constituted our cohort. FET-RF discriminated significantly between tumor and non-tumor. The texture feature Small-Zone-Low-Gray-Level-Emphasis (SZLGE) showed the best performance for the prediction of TTP (p = 0.001, satisfying Bonferroni-multiple-test significance level). Additionally, two radiomics signatures could predict TTP (TTP-radiomics-signature, p = 0.001) and OS (OS-radiomics-signature, p = 0.038). SZLGE and the TTP-radiomics-signature additionally predicted RL. Specifically, high values for TTP-radiomics-signature and for SZLGE indicated not only earlier progression, but also a RL within the initial FET-PET active volume.
conclusionOur findings suggest that FET-PET radiomics could contribute to the prognostic assessment and selection of rGBM-patients benefiting from re-irradiation. Trial registration DRKS00000633. Registered on 8th of December in 2010. https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00000633 .
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