Mustafa Yildirim1, Murat Baykara2. 1. Department of Radiology, Elazığ Fethi Sekin City Hospital, University of Health Sciences, Elazığ, Turkey. Mustafa23468@outlook.com. 2. Department of Radiology, Faculty of Medicine, Fırat University, Elazığ, Turkey.
Abstract
PURPOSE: Conventional 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. METHODS: Forty-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. RESULTS: Mean, 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. CONCLUSION: MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.
PURPOSE: Conventional 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. METHODS: Forty-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. RESULTS: Mean, 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. CONCLUSION: MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.
Authors: Y W Park; K Han; S S Ahn; Y S Choi; J H Chang; S H Kim; S-G Kang; E H Kim; S-K Lee Journal: AJNR Am J Neuroradiol Date: 2018-03-08 Impact factor: 3.825
Authors: Roger Stupp; Warren P Mason; Martin J van den Bent; Michael Weller; Barbara Fisher; Martin J B Taphoorn; Karl Belanger; Alba A Brandes; Christine Marosi; Ulrich Bogdahn; Jürgen Curschmann; Robert C Janzer; Samuel K Ludwin; Thierry Gorlia; Anouk Allgeier; Denis Lacombe; J Gregory Cairncross; Elizabeth Eisenhauer; René O Mirimanoff Journal: N Engl J Med Date: 2005-03-10 Impact factor: 91.245
Authors: Jigisha P Thakkar; Therese A Dolecek; Craig Horbinski; Quinn T Ostrom; Donita D Lightner; Jill S Barnholtz-Sloan; John L Villano Journal: Cancer Epidemiol Biomarkers Prev Date: 2014-07-22 Impact factor: 4.254
Authors: Roger Stupp; Monika E Hegi; Warren P Mason; Martin J van den Bent; Martin J B Taphoorn; Robert C Janzer; Samuel K Ludwin; Anouk Allgeier; Barbara Fisher; Karl Belanger; Peter Hau; Alba A Brandes; Johanna Gijtenbeek; Christine Marosi; Charles J Vecht; Karima Mokhtari; Pieter Wesseling; Salvador Villa; Elizabeth Eisenhauer; Thierry Gorlia; Michael Weller; Denis Lacombe; J Gregory Cairncross; René-Olivier Mirimanoff Journal: Lancet Oncol Date: 2009-03-09 Impact factor: 41.316