ArticleJournal of neuro-oncology2018
Prediction of survival in patients affected by glioblastoma: histogram analysis of perfusion MRI.
Article in Journal of neuro-oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
10 citing papers in PubMed.
- Higher cerebral metabolic rate of oxygen derived from dynamic susceptibility contrast MRI is an independent predictor of poor survival in IDH-wildtype glioblastoma.Neuroradiology · 2026Article
- Whole-lesion iodine map histogram analysis in the risk classification of gastrointestinal stromal tumors: comparison with single-slice iodine concentration measurements.Abdominal radiology (New York) · 2024Article
- Deep learning based on dynamic susceptibility contrast MR imaging for prediction of local progression in adult-type diffuse glioma (grade 4).Scientific reports · 2023Article
- Challenges and opportunities for advanced neuroimaging of glioblastoma.The British journal of radiology · 2023Review
- Prognostication of overall survival in patients with brain metastases using diffusion tensor imaging and dynamic susceptibility contrast-enhanced MRI.The British journal of radiology · 2022Article
- Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media.Pharmaceutics · 2022Review
- Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM.Journal of personalized medicine · 2021Article
- AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?Frontiers in oncology · 2021Article
- Association of dynamic susceptibility magnetic resonance imaging at initial tumor diagnosis with the prognosis of different molecular glioma subtypes.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2020Article
- Higher vascularity at infiltrated peripheral edema differentiates proneural glioblastoma subtype.PloS one · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe identification of prognostic biomarkers plays a pivotal role in the management of glioblastoma. The aim of this study was to assess the role of magnetic resonance dynamic susceptibility contrast imaging (DSC-MRI) with histogram analysis in the prognostic evaluation of patients suffering from glioblastoma. MATERIALS AND
methodsSixty-eight patients with newly diagnosed pathologically verified GBM were retrospectively evaluated. All patients underwent MRI investigations, including DSC-MRI, surgical procedure and received postoperative focal radiotherapy plus daily temozolomide (TMZ), followed by adjuvant TMZ therapy. Relative cerebral blood volume (rCBV) histograms were generated from a volume of interest covering the solid portions of the tumor and statistically evaluated for kurtosis, skewness, mean, median and maximum value of rCBV. To verify if histogram parameters could predict survival at 1 and 2 years, receiver operating characteristic (ROC) curves were obtained. Kaplan-Meier method was used to calculate patient's overall survival.
resultsrCBV kurtosis and rCBV skewness showed significant differences between subjects surviving > 1 and > 2 years, According to ROC analysis, the rCBV kurtosis showed the best statistic performance compared to the other parameters; respectively, values of 1 and 2.45 represented an optimised cut-off point to distinguish subjects surviving over 1 or 2 years. Kaplan-Meier curves showed a significant difference between subjects with rCBV kurtosis values higher or lower than 1 (respectively 1021 and 576 days; Log-rank test: p = 0.007), and between subjects with rCBV kurtosis values higher or lower than 2.45 (respectively 802 and 408 days; Log-rank test: p = 0.001).
conclusionThe histogram analysis of perfusion MRI proved to be a valid method to predict survival in patients affected by glioblastoma.
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