ArticleActa neurologica Belgica2022
Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma.
Article in Acta neurologica Belgica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- A novel synthetic MRI for discrimination of oral cavity and oropharynx malignancy.BMC medical imaging · 2025Article
- Lymph Node Staging by CT Texture Analysis in Patients with Sigmoid Cancer.Journal of clinical practice and research · 2023Article
- Comparison of T2W FLAIR Images of Patients with Multiple Sclerosis and Ischemic Gliosis via Histogram Analysis.Journal of clinical practice and research · 2023Article
- Evaluation of the corpus callosum using magnetic resonance imaging histogram analysis in autism spectrum disorder.The neuroradiology journal · 2022Article
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Abstract
purposeConventional magnetic resonance imaging (MRI) technics are insufficient in the differentiation of tumor progression from post-treatment changes in patients with treated glioblastoma. Previous studies have suggested that histogram analysis is a useful tool in the assessment of treatment response in different cancer types. The aim of the study was to to evaluate the effectiveness of MRI histogram analysis in the differentiation of tumor progression from pseudoprogression in patients with treated glioblastoma.
methodsForty-six patients with glioblastoma who newly developed enhancing lesions following chemoradiation treatment were included in this retrospective study. Histogram analysis was performed from new enhancing lesions on T1-weighted contrast-enhanced MRI. Histogram analysis findings of patients with progression (23) and pseudoprogression (23) were compared.
resultsMean, minimum, median, maximum, standard deviation, variance, entropy, skewness, uniformity values were found to be significantly higher in progressive disease (p < 0.05). A receiver-operating characteristic (ROC) curve analysis was performed for mean value, and area under the curve (AUC) was found as 0.975. When the threshold value was selected as 528.86, two groups could be differentiated with 95.7% sensitivity and 87.0% specificity.
conclusionMRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.
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