Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma



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.


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.


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.


MRI histogram analysis can be used for the differentiation of progressive disease from pseudoprogression.

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  1. 1.

    Ohka F, Natsume A, Wakabayashi T (2012) Current trends in targeted therapies for glioblastoma multiforme. Neurol Res Int. 2012:878425. https://doi.org/10.1155/2012/878425

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Thakkar JP, Dolecek TA, Horbinski C et al (2014) Epidemiologic and molecular prognostic review of Glioblastoma. Cancer Epidemiol Biomarkers Prev 23:1985–1985. https://doi.org/10.1158/1055-9965.EPI-14-0275

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Stupp R, Hegi ME, Mason WP, van den Bent MJ, Taphoorn MJB, Janzer RC, Ludwin SK, Allgeier A, Fisher B, Belanger K, Hau P, Brandes AA, Gijtenbeek J, Marosi C, Vecht CJ, Mokhtari K, Wesseling P, Villa S, Eisenhauer E, Gorlia T, Weller M, Lacombe D, Cairncross JG, Mirimanof R-O (2009) Efects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 10(5):459–466. https://doi.org/10.1016/S1470-2045(09)70025-7

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJB, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanof RO (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352(10):987–996. https://doi.org/10.1056/NEJMoa043330

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Thust SC, van den Bent MJ, Smits M (2018) Pseudoprogression of brain tumors. J Magn Reson Imaging 48(3):571–589. https://doi.org/10.1002/jmri.26171

    Article  PubMed Central  Google Scholar 

  6. 6.

    Melguizo-Gavilanes I, Bruner JM, Guha-Thakurta N, Hess KR, Puduvalli VK (2015) Characterization of pseudoprogression in patients with glioblastoma: is histology the gold standard? J Neurooncol 123(1):141–150. https://doi.org/10.1007/s11060-015-1774-5

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Park YW, Han K, Ahn SS et al (2018) Whole-tumor histogram and texture analyses of DTI for evaluation of IDH1-mutation and 1p/19q-codeletion status in world health organization grade II gliomas. AJNR Am J Neuroradiol 39:693–698. https://doi.org/10.3174/ajnr.A5569

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Yu H, Caldwell C, Mah K et al (2009) Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. Int J Radiat Oncol Biol Phys 75:618–625. https://doi.org/10.1016/j.ijrobp.2009.04.043

    Article  PubMed  Google Scholar 

  9. 9.

    Ganeshan B, Miles KA, Young RC, Chatwin CR (2009) Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol 70:101–110. https://doi.org/10.1016/j.ejrad.2007.12.005

    Article  PubMed  Google Scholar 

  10. 10.

    Suo ST, Zhuang ZG, Cao MQ et al (2016) Differentiation of pyogenic hepatic abscesses from malignant mimickers using multislice-based texture acquired from contrast-enhanced computed tomography. Hepatobiliary Pancreat Dis Int 15:391–398. https://doi.org/10.1016/s1499-3872(15)60031-5

    Article  PubMed  Google Scholar 

  11. 11.

    Castellano G, Bonilha L, Li LM, Cendes F (2004) Texture analysis of medical images. Clin Radiol 59:1061–1069. https://doi.org/10.1016/j.crad.2004.07.008

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Alic L, Niessen WJ, Veenland JF (2014) Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS ONE 9:e110300. https://doi.org/10.1371/journal.pone.0110300

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Yang D, Rao G, Martinez J, Veeraraghavan A, Rao A (2015) Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Med Phys 42:6725–6735. https://doi.org/10.1118/1.4934373

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Molina D, Perez-Beteta J, Luque B et al (2016) Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival. Br J Radiol. https://doi.org/10.1259/bjr.20160242

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Colombi D, Dinkel J, Weinheimer O et al (2015) Visual vs fully automatic histogram-based assessment of idiopathic pulmonary fibrosis (IPF) progression using sequential multidetector computed tomography (MDCT). PLoS ONE 10:e0130653. https://doi.org/10.1371/journal.pone.0130653

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Young RJ, Gupta A, Shah AD, Graber JJ, Zhang Z, Shi W et al (2011) Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblasto-ma. Neurology 76:1918–1924. https://doi.org/10.1212/WNL.0b013e31821d74e7

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Nelson DA, Tan TT, Rabson AB et al (2004) Hypoxia and defective apoptosis drive genomic instability and tumorigenesis. Genes Dev 18:2095–2107. https://doi.org/10.1101/gad.1204904

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Peng SL, Chen CF, Liu HL et al (2013) Analysis of parametric histogram from dynamic contrast-enhanced MRI: application in evaluating brain tumor response to radiotherapy. NMR Biomed 26:443–450. https://doi.org/10.1002/nbm.2882

    Article  PubMed  Google Scholar 

  19. 19.

    Liu Y, Zhang X, Feng N et al (2018) The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis. Acta Radiol 59:1239–1246. https://doi.org/10.1177/0284185118756951

    Article  PubMed  Google Scholar 

  20. 20.

    Yun TJ, Park C-K, Kim TM et al (2015) Glioblastoma treated with concurrent radiation therapy and temozolomide chemotherapy: differentiation of true progression from pseudoprogression with quantitative dynamic contrast-enhanced MR ımaging. Radiology. https://doi.org/10.1148/radiol.14132632

    Article  PubMed  Google Scholar 

  21. 21.

    Ng F, Ganeshan B, Kozarski R et al (2013) Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266:177–184. https://doi.org/10.1148/radiol.12120254

    Article  PubMed  Google Scholar 

  22. 22.

    Law M, Young R, Babb J, et al (2007) Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 28:761–66. http://www.ajnr.org/content/28/4/761.full

  23. 23.

    Beig N, Patel J, Prasanna P et al (2017) Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in glioblastoma. Med Imag. https://doi.org/10.1117/12.2255694

    Article  Google Scholar 

  24. 24.

    Kurtul N, Baykara M (2018) The association between MRI texture analysis and chemoradiotherapy outcomes in glioblastoma cases. Ann Med Res 25(4):1. https://doi.org/10.5455/annalsmedres.2018.09.191

    Article  Google Scholar 

  25. 25.

    Cao Y, Tsien CI, Nagesh V, Junck L, Ten Haken R, Ross BD et al (2006) Survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT. Int J Radiat Oncol Biol Phys 64:876–885. https://doi.org/10.1016/j.ijrobp.2005.09.001

    Article  PubMed  Google Scholar 

  26. 26.

    Kong DS, Kim ST, Kim EH et al (2011) Diagnostic dilemma of pseudoprogression in the treatment of newly diagnosed glioblastomas: the role of assessing relative cerebral blood flow volume and oxygen-6-methylguanine-DNA methyltransferase promoter methylation status. AJNR Am J Neuroradiol 32:382–387. https://doi.org/10.3174/ajnr.A2286

    Article  PubMed  Google Scholar 

  27. 27.

    Kim HS, Kim JH, Kim SH et al (2010) Posttreatment high-grade glioma: usefulness of peak height position with semiquantitative MR perfusion histogram analysis in an entire contrast-enhanced lesion for predicting volume fraction of recurrence. Radiology 256:906–915. https://doi.org/10.1148/radiol.10091461

    Article  PubMed  Google Scholar 

  28. 28.

    Yoshii Y (2008) Pathological review of late cerebral radionecrosis. Brain Tumor Pathol 25:51–58. https://doi.org/10.1007/s10014-008-0233-9

    Article  PubMed  Google Scholar 

  29. 29.

    Baek HJ, Kim HS, Kim N et al (2012) Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. Radiology 264:834–843. https://doi.org/10.1148/radiol.12112120

    Article  PubMed  Google Scholar 

  30. 30.

    Chu HH, Choi SH, Ryoo I et al (2015) Differentiation of true progression from pseudoprogression in glioblastoma treated with radiation therapy and concomitant temozolomide: comparison study of standard and high-b-value diffusion-weighted ımaging. Radiology. https://doi.org/10.1148/radiol.13122024

    Article  PubMed  Google Scholar 

  31. 31.

    Cha J, Kim ST, Kim H-J et al (2014) Differentiation of tumor progression from pseudoprogression in patients with posttreatment glioblastoma using multiparametric histogram analysis. AJNR Am J Neuroradiol 35(7):1309–1317. https://doi.org/10.3174/ajnr.A3876

    CAS  Article  PubMed  Google Scholar 

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Correspondence to Mustafa Yildirim.

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Yildirim, M., Baykara, M. Differentiation of progressive disease from pseudoprogression using MRI histogram analysis in patients with treated glioblastoma. Acta Neurol Belg (2021). https://doi.org/10.1007/s13760-021-01607-3

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  • Glioblastoma
  • Progression
  • Pseudoprogression
  • Histogram analysis