ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2018
Semantic imaging features predict disease progression and survival in glioblastoma multiforme patients.
Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ten Years of VASARI Glioma Features: Systematic Review and Meta-Analysis of Their Impact and Performance.AJNR. American journal of neuroradiology · 2024Pooled it
- Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.Neurosurgical review · 2021Pooled it
- MRI semantic features as prognostic indicators and biological mechanism insights in glioblastoma multiforme.Neurosurgical review · 2026Article
- Opportunities and challenges in application of VASARI features in neuro-oncology.Neuro-oncology practice · 2025Review
- Assessing inter-rater reliability of MRI features in glioma: a multi-radiologist agreement study.BMC medical imaging · 2025Article
- Preoperative MR - based model for predicting prognosis in patients with intracranial extraventricular ependymoma.European journal of radiology open · 2025Article
- Efficacy and safety of simultaneous integrated boost intensity-modulated radiotherapy combined with temozolomide for the postoperative chemotherapy treatment of multifocal high-grade glioma.Frontiers in oncology · 2025Article
- Association between dichotomized VASARI feature and overall survival in glioblastoma patients: a single-institution propensity score matching analysis.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- Nomogram incorporating preoperative MRI-VASARI features for differentiating intracranial extraventricular ependymoma from glioblastoma.Quantitative imaging in medicine and surgery · 2024Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Imaging predictors of 4q12 amplified and RB1 mutated glioblastoma IDH-wildtype.Journal of neuro-oncology · 2024Article
- The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study.Journal of neuro-oncology · 2024Article
- The importance of planning CT-based imaging features for machine learning-based prediction of pain response.Scientific reports · 2023Article
- Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi-center cohort study of 114 cases.Cancer medicine · 2023Article
- Current role of machine learning and radiogenomics in precision neuro-oncology.Exploration of targeted anti-tumor therapy · 2023Review
- Current Evidence, Limitations and Future Challenges of Survival Prediction for Glioblastoma Based on Advanced Noninvasive Methods: A Narrative Review.Medicina (Kaunas, Lithuania) · 2022Review
- Noninvasive Determination of the IDH Status of Gliomas Using MRI and MRI-Based Radiomics: Impact on Diagnosis and Prognosis.Current oncology (Toronto, Ont.) · 2022Review
- CT-Based Radiomics Nomogram Improves Risk Stratification and Prediction of Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy.Frontiers in oncology · 2022Article
- Prognostic Assessment in High-Grade Soft-Tissue Sarcoma Patients: A Comparison of Semantic Image Analysis and Radiomics.Cancers · 2021Article
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6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor glioblastoma (GBM), multiple prognostic factors have been identified. Semantic imaging features were shown to be predictive for survival prediction. No similar data have been generated for the prediction of progression. The aim of this study was to assess the predictive value of the semantic visually accessable REMBRANDT [repository for molecular brain neoplasia data] images (VASARI) imaging feature set for progression and survival, and the creation of joint prognostic models in combination with clinical and pathological information.
methods189 patients were retrospectively analyzed. Age, Karnofsky performance status, gender, and MGMT promoter methylation and IDH mutation status were assessed. VASARI features were determined on pre- and postoperative MRIs. Predictive potential was assessed with univariate analyses and Kaplan-Meier survival curves. Following variable selection and resampling, multivariate Cox regression models were created. Predictive performance was tested on patient test sets and compared between groups. The frequency of selection for single variables and variable pairs was determined.
resultsFor progression free survival (PFS) and overall survival (OS), univariate significant associations were shown for 9 and 10 VASARI features, respectively. Multivariate models yielded concordance indices significantly different from random for the clinical, imaging, combined, and combined + MGMT models of 0.657, 0.636, 0.694, and 0.716 for OS, and 0.602, 0.604, 0.633, and 0.643 for PFS. "Multilocality," "deep white-matter invasion," "satellites," and "ependymal invasion" were over proportionally selected for multivariate model generation, underlining their importance.
conclusionsWe demonstrated a predictive value of several qualitative imaging features for progression and survival. The performance of prognostic models was increased by combining clinical, pathological, and imaging features.
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